Project 2: Data Wrangling and Analysis¶

The dataset in on WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because "they're good dogs Brent." WeRateDogs has over 4 million followers and has received international media coverage.
Three different dataset are used

1. Enhanced Twitter Archive¶
The WeRateDogs Twitter archive contains basic tweet data for all 5000+ of their tweets, but not everything. One column the archive does contain though: each tweet's text, which I used to extract rating, dog name, and dog "stage" (i.e. doggo, floofer, pupper, and puppo) to make this Twitter archive "enhanced."

2. Additional Data via the Twitter API¶

Used twitter api to scrape on each twet_Id on the Enhanced Twitter Archive to get retweet count and favourite count of each tweet id.

3. The tweet image predictions¶
A table full of image predictions (the top three only) alongside each tweet ID, image URL, and the image number that corresponded to the most confident prediction (numbered 1 to 4 since tweets can have up to four images).Used request library to download the dataset from udacity using this url[https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv]

import tweepy

import json
import pandas as pd
import numpy as np
from timeit import default_timer as timer


import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline
sns.set_style("darkgrid")

# settings to display all columns
pd.set_option("display.max_columns", None)
pd.set_option('display.max_rows', None)  # or 1000

# Variables that contains the credentials to access Twitter API
ACCESS_TOKEN = 'xxxxxxxxxx'
ACCESS_SECRET = 'xxxxxxxxx'
CONSUMER_KEY = 'xxxxxxxxx'
CONSUMER_SECRET = 'xxxxxxxxx'


# Setup access to API
def connect_to_twitter_OAuth():
    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
    auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)

    api = tweepy.API(auth)
    return api


# Create API object
api = connect_to_twitter_OAuth()

# tweets from my stream
public_tweets = api.home_timeline()
for tweet in public_tweets:
    print(tweet.text)

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Read on Enhanced Twitter Archive.¶

archive_df = pd.read_csv('twitter-archive-enhanced.csv')
archive_df.head()

archive_df.shape

(2356, 17)

# the ID of the status
id = 891815181378084864
  
# fetching the status
status = api.get_status(id)
  
# fetching the retweet_count attribute
retweet_count = status.retweet_count
  
print("The number of time the status has been retweeted is : " + str(retweet_count))

The number of time the status has been retweeted is : 3477

for i in archive_df['tweet_id']:
    #tweet_id = data['tweet_id']
    
    # fetching the status
    status = api.get_status(i)
    
    # fetching the retweet_count attribute
    retweet_count = status.retweet_count
    print(i)
    print(retweet_count)
    
    likes = status.favorite_count
    print(likes)
    
    data_collected ={
        'tweet_id': i,
        'retweet_count': retweet_count,
        'likes': likes
    }
    
    break

892420643555336193
7001
33801

# Query Twitter's API for JSON data for each tweet ID in the Twitter archive
count = 0
fails_dict = {}
start = timer()
missing_tweets = []
# Save each tweet's returned JSON as a new line in a .txt file
with open('tweet_json.txt', 'w') as outfile:
    # This loop will likely take 20-30 min to run 
    for tweet_id in archive_df['tweet_id']:
        count += 1
        print(str(count) + ": " + str(tweet_id))
        try:
            tweet = api.get_status(tweet_id, tweet_mode = 'extended',
                                   wait_on_rate_limit = True,
                                   wait_on_rate_limit_notify = True)
            
            print("Success")
            json.dump(tweet._json, outfile)
            outfile.write('\n')
        except Exception as err_msg:
            print(str(tweet_id)+ " - " + str(err_msg))
            missing_tweets.append(tweet_id)
        
        
end = timer()
print('Entire query twitter data take {} min'.format(
        (end - start)/60))
print(fails_dict)

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

1: 892420643555336193

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

Success
2: 892177421306343426

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

Success
3: 891815181378084864

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

Success
4: 891689557279858688

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

Success
5: 891327558926688256

len(missing_tweets)

29

missing_tweets_final = [] # A list for the final unretrieved tweetIDs

with open('tweet_json.txt', 'a', encoding='utf8') as file:
    for tweet_id in missing_tweets:
        print(str(count) + ": " + str(tweet_id))
        try:
            tweet = api.get_status(tweet_id, tweet_mode = 'extended',
                                  wait_on_rate_limit = True, 
                                  wait_on_rate_limit_notify = True)
            
            print("Success")
            json.dump(tweet._json, outfile)
            outfile.write('\n')
            
        except Exception as err_msg:
            print(str(tweet_id)+ " - " + str(err_msg))
            missing_tweets_final.append(tweet_id)

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

2356: 888202515573088257

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

888202515573088257 - 404 Not Found
144 - No status found with that ID.
2356: 873697596434513921

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

873697596434513921 - 404 Not Found
144 - No status found with that ID.
2356: 872668790621863937

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

872668790621863937 - 404 Not Found
144 - No status found with that ID.
2356: 872261713294495745

Unexpected parameter: wait_on_rate_limit
Unexpected parameter: wait_on_rate_limit_notify

872261713294495745 - 404 Not Found
144 - No status found with that ID.
2356: 869988702071779329

len(missing_tweets_final)

29

Seems 30 tweets from the archive_df are no-longer avaliable

# Collect all selected attributes
selected_attr = []

with open('tweet_json.txt', 'r') as json_file:
    for line in json_file:
        json_data = json.loads(line)
        
        selected_attr.append({
            'tweet_id': json_data['id_str'],
            'retweet_count': json_data['retweet_count'],
            'favorite_count': json_data['favorite_count'],
        })
        

# Save all selected attributes in another dataframe
api_df = pd.DataFrame(selected_attr, columns = [
    'tweet_id', 'retweet_count', 'favorite_count'
])

api_df.head()

api_df.shape

(2327, 3)

The tweet image predictions¶

Used request to download the dataset.

import requests
import csv
res = requests.get('https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv')
with open('image_predictions.tsv', mode='wb') as file:
    file.write(res.content)

image = pd.read_csv('image_predictions.tsv', sep='\t')
image.head()

image

archive_df

archive_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   tweet_id                    2356 non-null   int64  
 1   in_reply_to_status_id       78 non-null     float64
 2   in_reply_to_user_id         78 non-null     float64
 3   timestamp                   2356 non-null   object 
 4   source                      2356 non-null   object 
 5   text                        2356 non-null   object 
 6   retweeted_status_id         181 non-null    float64
 7   retweeted_status_user_id    181 non-null    float64
 8   retweeted_status_timestamp  181 non-null    object 
 9   expanded_urls               2297 non-null   object 
 10  rating_numerator            2356 non-null   int64  
 11  rating_denominator          2356 non-null   int64  
 12  name                        2356 non-null   object 
 13  doggo                       2356 non-null   object 
 14  floofer                     2356 non-null   object 
 15  pupper                      2356 non-null   object 
 16  puppo                       2356 non-null   object 
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB

archive_df[['rating_numerator', 'rating_denominator']].describe().T

archive_df['name'].value_counts()

None              745
a                  55
Charlie            12
Cooper             11
Lucy               11
Oliver             11
Penny              10
Tucker             10
Lola               10
Bo                  9
Winston             9
Sadie               8
the                 8
Buddy               7
Daisy               7
Toby                7
an                  7
Bailey              7
Bella               6
Rusty               6
Dave                6
Koda                6
Jax                 6
Milo                6
Oscar               6
Jack                6
Leo                 6
Scout               6
Stanley             6
Phil                5
Oakley              5
Chester             5
very                5
Larry               5
Alfie               5
Louis               5
Sammy               5
Bentley             5
Gus                 5
Sunny               5
Finn                5
George              5
Luna                4
Clark               4
Beau                4
Maggie              4
just                4
Sampson             4
Jerry               4
Archie              4
quite               4
Reginald            4
Jeffrey             4
Moose               4
Chip                4
Loki                4
Boomer              4
Shadow              4
Clarence            4
Maddie              4
Reggie              4
Ruby                4
Winnie              4
Dexter              4
Walter              4
Gerald              4
Bruce               4
one                 4
Scooter             4
Derek               4
Riley               4
Gary                4
Maximus             4
Sophie              4
Bear                4
Cassie              4
Brody               4
Hank                4
Carl                4
Duke                4
Nala                3
Ted                 3
Colby               3
Kyle                3
Coco                3
Zoey                3
Jimothy             3
Peaches             3
Olive               3
Mia                 3
Malcolm             3
Max                 3
Zeke                3
Ellie               3
Lily                3
Wyatt               3
Calvin              3
Klevin              3
Wallace             3
Lorenzo             3
Sebastian           3
Steven              3
Waffles             3
Louie               3
Wilson              3
Paisley             3
Vincent             3
Otis                3
Reese               3
Rosie               3
Arnie               3
Frankie             3
Earl                3
Rory                3
Gizmo               3
Samson              3
Doug                3
Luca                2
Franklin            2
Alice               2
Butter              2
Gromit              2
Pickles             2
Jeph                2
Chuckles            2
Cody                2
Phineas             2
Finley              2
Doc                 2
Lou                 2
Titan               2
Solomon             2
Olivia              2
Sierra              2
Bungalo             2
Stubert             2
Blitz               2
Cash                2
Thumas              2
Abby                2
Hammond             2
Kyro                2
Jimison             2
Trooper             2
Keith               2
Aspen               2
Philbert            2
Griffin             2
Yogi                2
Coops               2
Balto               2
Levi                2
Kirby               2
Smokey              2
Rufus               2
Bell                2
Gabe                2
Chipson             2
Rizzy               2
Kilo                2
Flávio              2
Sarge               2
Fiona               2
Charles             2
Tyr                 2
Hobbes              2
Anakin              2
Nelly               2
Jiminy              2
Albert              2
Misty               2
Albus               2
Eve                 2
Moe                 2
Paull               2
Davey               2
Baxter              2
Hurley              2
Canela              2
Wally               2
Kenny               2
Odie                2
Terry               2
Belle               2
Bubbles             2
Jamesy              2
Rocky               2
Logan               2
Meyer               2
Quinn               2
Moreton             2
Kreg                2
getting             2
Patrick             2
Mattie              2
Bisquick            2
Atticus             2
Neptune             2
Bob                 2
Fizz                2
Opal                2
Rocco               2
Kenneth             2
Tyrone              2
Benedict            2
mad                 2
Oshie               2
Dakota              2
CeCe                2
Hercules            2
Kevin               2
Pablo               2
Lennon              2
Klein               2
Brad                2
Timison             2
Atlas               2
Happy               2
Elliot              2
Ollie               2
Sandy               2
Piper               2
Rubio               2
Chet                2
Ava                 2
Churlie             2
Axel                2
actually            2
Nollie              2
Frank               2
Ken                 2
Theodore            2
Remington           2
Sugar               2
Crystal             2
Ozzy                2
Harold              2
Betty               2
Indie               2
Linda               2
Pipsy               2
Django              2
Raymond             2
Pippa               2
Dash                2
Marley              2
Jesse               2
Percy               2
Lenny               2
Ash                 2
Herald              2
Emmy                2
Penelope            2
Mister              2
Curtis              2
Lincoln             2
Fred                2
Herm                2
Keurig              2
Sansa               2
Hunter              2
Seamus              2
Cali                2
Leela               2
Shaggy              2
Cupcake             2
Chompsky            2
Juno                2
Eli                 2
Layla               2
Terrance            2
Lilly               2
Carly               2
Jackson             2
Harper              2
Maxaroni            2
Benji               2
Bernie              2
Sam                 2
Chelsea             2
Herschel            2
Baloo               2
Panda               2
Dawn                2
Gidget              2
Roosevelt           2
Watson              2
Astrid              2
Phred               2
Calbert             2
not                 2
Romeo               2
Kreggory            2
Stephan             2
Oliviér             2
Snoopy              1
Mac                 1
Kendall             1
old                 1
Kane                1
Wafer               1
Flurpson            1
Combo               1
Chubbs              1
Goliath             1
Dudley              1
Poppy               1
Tuck                1
Jarod               1
Shadoe              1
Kramer              1
Bubba               1
Kayla               1
Angel               1
Nugget              1
officially          1
Carper              1
Cermet              1
Grizzwald           1
Link                1
Frönq               1
Blipson             1
Gunner              1
Mairi               1
Grady               1
Snickers            1
Akumi               1
Zeus                1
Suki                1
Kuyu                1
Ferg                1
Iggy                1
Hamrick             1
Aja                 1
Clybe               1
all                 1
Crumpet             1
Aubie               1
Teddy               1
Ridley              1
Mason               1
Ike                 1
Snoop               1
Beckham             1
Storkson            1
Emmie               1
Thor                1
Charl               1
Dale                1
Ember               1
Charleson           1
Dot                 1
Remy                1
Kathmandu           1
Christoper          1
Tom                 1
Shakespeare         1
Scruffers           1
Divine              1
Pluto               1
Tupawc              1
Obi                 1
Sage                1
Kaiya               1
Sid                 1
Adele               1
Kanu                1
Maude               1
Birf                1
Ralphus             1
Kellogg             1
Blakely             1
Rooney              1
Kial                1
Molly               1
Hubertson           1
infuriating         1
Newt                1
Danny               1
Timber              1
Huxley              1
Ronnie              1
Hall                1
Jeffri              1
Geoff               1
Murphy              1
Godzilla            1
Rufio               1
Fabio               1
Geno                1
Trevith             1
Lassie              1
Timofy              1
Bert                1
Rey                 1
Jeb                 1
Arya                1
Humphrey            1
Kobe                1
Glenn               1
Cheesy              1
Tito                1
Chef                1
Hero                1
Willie              1
Izzy                1
Lulu                1
Jaspers             1
Eevee               1
incredibly          1
Margo               1
Willem              1
Oreo                1
Burt                1
Jomathan            1
Jessiga             1
Boots               1
Amy                 1
Claude              1
Skye                1
Superpup            1
Ralph               1
Swagger             1
Lorelei             1
Crimson             1
Millie              1
Opie                1
Lillie              1
Lance               1
Cannon              1
Dallas              1
Liam                1
light               1
Ace                 1
Lizzie              1
Keet                1
Strudel             1
Gustav              1
Marlee              1
Fiji                1
Bobble              1
Rizzo               1
Wishes              1
Sunshine            1
Eriq                1
JD                  1
Kulet               1
Crawford            1
Brandy              1
Cal                 1
Emma                1
Peanut              1
Rontu               1
Jed                 1
General             1
Clifford            1
Miguel              1
Kloey               1
Dex                 1
Zooey               1
Tedders             1
Boston              1
Monty               1
Tanner              1
Steve               1
Tilly               1
Lugan               1
Zeek                1
Bronte              1
Ben                 1
Gilbert             1
Sweet               1
Alexanderson        1
Ito                 1
Sonny               1
Coleman             1
Blue                1
Venti               1
Reptar              1
Aiden               1
Karl                1
Michelangelope      1
Tug                 1
Major               1
Andy                1
Karll               1
Noosh               1
Spark               1
Pete                1
Alfy                1
Jazz                1
Duddles             1
Kota                1
Sparky              1
Willy               1
Maxwell             1
Mark                1
Rilo                1
Aqua                1
Vinscent            1
Ralf                1
Mabel               1
Socks               1
Taco                1
Maya                1
Mollie              1
Lenox               1
Godi                1
Bilbo               1
Meera               1
Julius              1
Dante               1
Fynn                1
Ebby                1
Rose                1
Marq                1
Pupcasso            1
Tommy               1
Edd                 1
Pawnd               1
Florence            1
Jonah               1
Clyde               1
Blu                 1
Daniel              1
Jim                 1
Berb                1
Blanket             1
Buckley             1
Rupert              1
Jay                 1
Rodney              1
Glacier             1
Loomis              1
Lilli               1
Mingus              1
Banjo               1
this                1
Stu                 1
Leonidas            1
Finnegus            1
Skittles            1
Ambrose             1
Ginger              1
Pilot               1
Mookie              1
Jebberson           1
Pubert              1
Juckson             1
Shelby              1
Yukon               1
Amélie              1
Simba               1
William             1
Corey               1
Herb                1
Jo                  1
Edmund              1
Trigger             1
Jimbo               1
Eugene              1
Rodman              1
Rumble              1
Jett                1
DayZ                1
Vinnie              1
Saydee              1
Beemo               1
Schnozz             1
Nico                1
his                 1
Katie               1
Pancake             1
Lilah               1
Cheryl              1
Doobert             1
Rumpole             1
Patch               1
Pippin              1
Ed                  1
Jaycob              1
Richie              1
Ron                 1
Dook                1
Grizz               1
Callie              1
Maks                1
Sky                 1
Wesley              1
Hanz                1
Harlso              1
Mutt                1
Pip                 1
Monkey              1
Iroh                1
Jersey              1
Fido                1
Duchess             1
my                  1
Stefan              1
Perry               1
Mike                1
Skittle             1
Pepper              1
Dug                 1
Cleopatricia        1
Laela               1
Coopson             1
Apollo              1
Sprout              1
Darrel              1
Dotsy               1
Gòrdón              1
Bruno               1
Gordon              1
Zoe                 1
Chuq                1
Lolo                1
Sandra              1
Rudy                1
Sweets              1
Dobby               1
Enchilada           1
Cedrick             1
Yoda                1
Chaz                1
Donny               1
Kaia                1
Aldrick             1
Derby               1
Sailer              1
Rueben              1
Harvey              1
Brat                1
Bloop               1
Roscoe              1
Ralphy              1
Kingsley            1
Durg                1
Tove                1
Jeffrie             1
Karma               1
Einstein            1
Benny               1
Howie               1
Joshwa              1
Buddah              1
Harnold             1
Barry               1
Rhino               1
Bookstore           1
Billl               1
Barclay             1
Brandonald          1
Carll               1
Harry               1
Sephie              1
Filup               1
Chesterson          1
Arlen               1
Chevy               1
Comet               1
Darla               1
life                1
Franq               1
Tater               1
Travis              1
Zuzu                1
Raphael             1
Horace              1
Scott               1
Samsom              1
Terrenth            1
Emanuel             1
Dido                1
Livvie              1
Marty               1
Ester               1
Wiggles             1
Luther              1
Dixie               1
Todo                1
Dunkin              1
Stormy              1
Tebow               1
Lucky               1
Biden               1
Siba                1
Miley               1
Antony              1
Bradley             1
Joey                1
Arnold              1
Augie               1
Nigel               1
BeBe                1
Jackie              1
Kody                1
Trip                1
Jarvis              1
Dutch               1
Tyrus               1
Flash               1
Ralphé              1
Halo                1
Beya                1
Chase               1
River               1
by                  1
Jennifur            1
Bode                1
Fillup              1
Mack                1
Lucia               1
Jameson             1
Henry               1
Meatball            1
Remus               1
Dwight              1
space               1
Harrison            1
Ole                 1
Stella              1
Spencer             1
Snicku              1
Julio               1
Acro                1
Ruffles             1
Beebop              1
Ralphson            1
Olaf                1
Bauer               1
Tiger               1
Brudge              1
Cora                1
Cuddles             1
Tassy               1
Dewey               1
Oddie               1
Noah                1
Tino                1
Ralpher             1
Koko                1
Erik                1
Rolf                1
Damon               1
Rascal              1
Grizzie             1
Severus             1
Staniel             1
Marvin              1
Bertson             1
Gin                 1
Bodie               1
Ronduh              1
Rinna               1
Walker              1
Mauve               1
Tess                1
Dylan               1
Tedrick             1
Gerbald             1
Jareld              1
Mimosa              1
Brandi              1
Ivar                1
Mary                1
Pavlov              1
Lipton              1
Jessifer            1
Autumn              1
Kevon               1
Cilantro            1
Odin                1
Kallie              1
Freddery            1
Tessa               1
Bobbay              1
Jiminus             1
Obie                1
Ashleigh            1
Alf                 1
Diogi               1
Ziva                1
Lacy                1
Hector              1
Kona                1
Barney              1
Brownie             1
Deacon              1
Moofasa             1
Milky               1
Bonaparte           1
Ozzie               1
Jazzy               1
Philippe            1
Kara                1
Ralphie             1
Jordy               1
Leonard             1
Longfellow          1
Brady               1
Puff                1
Binky               1
Vixen               1
Torque              1
Tuco                1
Brian               1
Schnitzel           1
O                   1
Spanky              1
Napolean            1
Stuart              1
Monster             1
Jerome              1
Banditt             1
Sprinkles           1
Bloo                1
Bowie               1
Striker             1
Pinot               1
Zara                1
Bobb                1
Fwed                1
Shawwn              1
Edgar               1
Tango               1
Maisey              1
Eazy                1
Champ               1
Jangle              1
Rorie               1
Alexander           1
Naphaniel           1
Cupid               1
Chadrick            1
Chesney             1
Sobe                1
DonDon              1
Mona                1
Arlo                1
Mojo                1
Georgie             1
Andru               1
Tobi                1
Jockson             1
Mya                 1
Holly               1
Tayzie              1
Kollin              1
Shnuggles           1
Toffee              1
Kawhi               1
Brockly             1
Billy               1
Asher               1
Rambo               1
Linus               1
Eleanor             1
Reagan              1
Sojourner           1
Tycho               1
Anthony             1
Jeremy              1
Orion               1
Stewie              1
Norman              1
Heinrich            1
Darby               1
Stark               1
Huck                1
Mitch               1
Berkeley            1
Bluebert            1
Grey                1
Stephanus           1
Carbon              1
Strider             1
Gustaf              1
Clarq               1
Bones               1
Caryl               1
Carter              1
Amber               1
Chuck               1
Baron               1
Bobby               1
Covach              1
Evy                 1
Vince               1
Lambeau             1
Shiloh              1
Malikai             1
Griswold            1
Ricky               1
Rover               1
Alejandro           1
Anna                1
Pherb               1
Creg                1
Clarkus             1
Taz                 1
Fletcher            1
Lupe                1
Goose               1
Brutus              1
Hermione            1
Sailor              1
Winifred            1
Bradlay             1
Sully               1
Sora                1
Mo                  1
Tripp               1
Al                  1
Laika               1
Dietrich            1
Kirk                1
Chloe               1
Timmy               1
Smiley              1
Gert                1
Cecil               1
Genevieve           1
unacceptable        1
Farfle              1
Hazel               1
Devón               1
Shikha              1
Crouton             1
Pumpkin             1
Batdog              1
Furzey              1
Gabby               1
Kenzie              1
Randall             1
Butters             1
Theo                1
Brooks              1
Bayley              1
Robin               1
Lili                1
Tonks               1
Mosby               1
such                1
Shooter             1
Sundance            1
Nida                1
Ulysses             1
Traviss             1
Willow              1
Craig               1
Petrick             1
Nimbus              1
Josep               1
Colin               1
Bruiser             1
Name: name, dtype: int64

archive_df.isna().sum()

tweet_id                         0
in_reply_to_status_id         2278
in_reply_to_user_id           2278
timestamp                        0
source                           0
text                             0
retweeted_status_id           2175
retweeted_status_user_id      2175
retweeted_status_timestamp    2175
expanded_urls                   59
rating_numerator                 0
rating_denominator               0
name                             0
doggo                            0
floofer                          0
pupper                           0
puppo                            0
dtype: int64

archive_df['doggo'].value_counts()

None     2259
doggo      97
Name: doggo, dtype: int64

archive_df['floofer'].value_counts()

None       2346
floofer      10
Name: floofer, dtype: int64

Image data¶

image

Image data¶

Visually¶

non matching names, 'shopping_cart' != 'shopping_basket'

image.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   tweet_id  2075 non-null   int64  
 1   jpg_url   2075 non-null   object 
 2   img_num   2075 non-null   int64  
 3   p1        2075 non-null   object 
 4   p1_conf   2075 non-null   float64
 5   p1_dog    2075 non-null   bool   
 6   p2        2075 non-null   object 
 7   p2_conf   2075 non-null   float64
 8   p2_dog    2075 non-null   bool   
 9   p3        2075 non-null   object 
 10  p3_conf   2075 non-null   float64
 11  p3_dog    2075 non-null   bool   
dtypes: bool(3), float64(3), int64(2), object(4)
memory usage: 152.1+ KB

image.describe()

p2_conf distribution seem to have an issue

sum(image.duplicated())

0

api_df¶

api_df

api_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2327 entries, 0 to 2326
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   tweet_id        2327 non-null   object
 1   retweet_count   2327 non-null   int64 
 2   favorite_count  2327 non-null   int64 
dtypes: int64(2), object(1)
memory usage: 54.7+ KB

Assessing¶

Quality:¶

archive_df¶

name column have 'a' only as a name and also have "None as a name.
Timestamp data type is not correct
several columns have missing data
Max values for rating_denominator and numerator seems extreme,
None values should be converted to NaN values
Remove html tags from source
Missing data images
arrchive dataset has retweeted data
many Id's columns

Tidyness(visually)¶

Dogtionary (explains the various stages of dog) is an observetion and it should be a row.
archive data and tweets api data shold be one table, both are(e.g each observation unit forms a table)
Image data should only have one column of the True predicted dogs and on column highest-confidence of a true prediction

#makes dataset copies
archive_clean = archive_df.copy()
image_clean = image.copy()
api_df_clean = api_df.copy()

Define¶

TImestamp column should be in datetime data type it's in 'object' datatype¶

code¶

archive_clean['timestamp'] = archive_clean['timestamp'].astype('datetime64[ns]')

test¶

archive_clean['timestamp'].dtype

dtype('<M8[ns]')

archive_clean.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype         
---  ------                      --------------  -----         
 0   tweet_id                    2356 non-null   int64         
 1   in_reply_to_status_id       78 non-null     float64       
 2   in_reply_to_user_id         78 non-null     float64       
 3   timestamp                   2356 non-null   datetime64[ns]
 4   source                      2356 non-null   object        
 5   text                        2356 non-null   object        
 6   retweeted_status_id         181 non-null    float64       
 7   retweeted_status_user_id    181 non-null    float64       
 8   retweeted_status_timestamp  181 non-null    object        
 9   expanded_urls               2297 non-null   object        
 10  rating_numerator            2356 non-null   int64         
 11  rating_denominator          2356 non-null   int64         
 12  name                        2356 non-null   object        
 13  doggo                       2356 non-null   object        
 14  floofer                     2356 non-null   object        
 15  pupper                      2356 non-null   object        
 16  puppo                       2356 non-null   object        
dtypes: datetime64[ns](1), float64(4), int64(3), object(9)
memory usage: 313.0+ KB

Define¶

None values should be converted to NaN values¶

none_columns = ['name', 'doggo', 'floofer', 'pupper', 'puppo']

for i in none_columns:
    archive_clean[i] = archive_clean[i].replace('None', np.nan)

archive_clean['puppo'].value_counts(dropna=False)

NaN      2326
puppo      30
Name: puppo, dtype: int64

archive_clean['floofer'].value_counts(dropna=False)

NaN        2346
floofer      10
Name: floofer, dtype: int64

Define¶

Remove html tags from source¶

archive_clean['source'][1]

'<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>'

code¶

import re

def cleanhtml(raw_html):
    cleanr = re.compile('<.*?>')
    cleantext = re.sub(cleanr, '', raw_html)
    return cleantext

archive_clean['source']  = archive_clean['source'].apply(cleanhtml)

Test¶

archive_clean['source'][1]

'Twitter for iPhone'

archive_clean['source'].value_counts()

Twitter for iPhone     2221
Vine - Make a Scene      91
Twitter Web Client       33
TweetDeck                11
Name: source, dtype: int64

Define¶

There is retweeted data in the archive dataset.(remove)¶

code¶

archive_clean = archive_clean[archive_clean['retweeted_status_id'].isna()]

test¶

archive_clean.shape

(2175, 17)

archive_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2175 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype         
---  ------                      --------------  -----         
 0   tweet_id                    2175 non-null   int64         
 1   in_reply_to_status_id       78 non-null     float64       
 2   in_reply_to_user_id         78 non-null     float64       
 3   timestamp                   2175 non-null   datetime64[ns]
 4   source                      2175 non-null   object        
 5   text                        2175 non-null   object        
 6   retweeted_status_id         0 non-null      float64       
 7   retweeted_status_user_id    0 non-null      float64       
 8   retweeted_status_timestamp  0 non-null      object        
 9   expanded_urls               2117 non-null   object        
 10  rating_numerator            2175 non-null   int64         
 11  rating_denominator          2175 non-null   int64         
 12  name                        1495 non-null   object        
 13  doggo                       87 non-null     object        
 14  floofer                     10 non-null     object        
 15  pupper                      234 non-null    object        
 16  puppo                       25 non-null     object        
dtypes: datetime64[ns](1), float64(4), int64(3), object(9)
memory usage: 305.9+ KB

Define¶

Drop tweets with missing images(expanded_urls shows tweets images)¶

archive_clean['expanded_urls'].isna().sum()

58

code¶

archive_clean = archive_clean[~archive_clean['expanded_urls'].isna()]

test¶

archive_clean['expanded_urls'].isna().sum()

0

archive_clean.shape

(2117, 17)

Define¶

Some dogs name are invalid¶

archive_clean.name.value_counts(dropna=False)

NaN               622
a                  55
Charlie            11
Lucy               11
Oliver             10
Cooper             10
Tucker              9
Penny               9
Lola                8
Winston             8
Sadie               8
the                 8
Toby                7
Daisy               7
Bo                  6
Stanley             6
Oscar               6
Koda                6
Bailey              6
an                  6
Jax                 6
Bella               6
Bentley             5
Milo                5
Rusty               5
Buddy               5
Scout               5
Chester             5
Leo                 5
Louis               5
Dave                5
Larry               4
Archie              4
Jerry               4
Jack                4
Alfie               4
Dexter              4
Boomer              4
one                 4
Phil                4
Scooter             4
Gary                4
Bear                4
Finn                4
Chip                4
Reggie              4
Maggie              4
Clark               4
very                4
Derek               4
Winnie              4
Oakley              4
Gus                 4
Duke                4
Sophie              4
Sammy               4
George              4
Clarence            4
Brody               4
Jeffrey             4
Cassie              4
Louie               3
Olive               3
Ruby                3
Jimothy             3
Mia                 3
Luna                3
Max                 3
Beau                3
Wallace             3
Klevin              3
Zeke                3
Sunny               3
Ellie               3
just                3
Ted                 3
Reginald            3
Nala                3
Shadow              3
Bruce               3
Hank                3
Gerald              3
Steven              3
Malcolm             3
Loki                3
Samson              3
Otis                3
Zoey                3
Kyle                3
Carl                3
Sebastian           3
Moose               3
Riley               3
Wilson              3
Wyatt               3
quite               3
Walter              3
Lily                3
Gizmo               3
Calvin              3
Waffles             3
Rosie               3
Vincent             3
Maximus             3
Reese               3
Earl                3
Doug                3
Sugar               2
Sansa               2
Panda               2
Coco                2
Bernie              2
Chuckles            2
Solomon             2
Peaches             2
Charles             2
Smokey              2
Flávio              2
Kilo                2
Doc                 2
Lou                 2
Titan               2
Misty               2
Albus               2
Moe                 2
Opal                2
Oliviér             2
Rocco               2
Fred                2
Lennon              2
Brad                2
Patrick             2
Sam                 2
Hunter              2
Dakota              2
Cash                2
Jimison             2
Keith               2
Aspen               2
Sarge               2
Griffin             2
Yogi                2
Coops               2
Rufus               2
Piper               2
Hammond             2
Trooper             2
Theodore            2
CeCe                2
Cody                2
Kenneth             2
Pippa               2
Rocky               2
Terry               2
Raymond             2
Mister              2
Juno                2
Arnie               2
Jiminy              2
Lincoln             2
getting             2
Atticus             2
Bob                 2
Kreggory            2
Bubbles             2
Herm                2
actually            2
Churlie             2
Luca                2
Atlas               2
Chipson             2
Kreg                2
Eli                 2
Alice               2
Blitz               2
Kirby               2
Belle               2
Fiona               2
Gabe                2
Nelly               2
Albert              2
Franklin            2
Thumas              2
Ash                 2
Colby               2
Odie                2
Wally               2
Jeph                2
Ollie               2
Jesse               2
Paisley             2
Finley              2
Layla               2
Crystal             2
Neptune             2
Frank               2
Elliot              2
Roosevelt           2
Rory                2
Chet                2
Ava                 2
Axel                2
Indie               2
Harold              2
Watson              2
Frankie             2
Linda               2
Maxaroni            2
Kevin               2
Dash                2
Marley              2
Calbert             2
Phineas             2
Pickles             2
Sampson             2
Django              2
Percy               2
Ozzy                2
Remington           2
Maddie              2
Tyr                 2
Baxter              2
Lenny               2
Benji               2
Bisquick            2
Oshie               2
Hobbes              2
Curtis              2
Olivia              2
Abby                2
Benedict            2
Jackson             2
Phred               2
Keurig              2
not                 2
Romeo               2
Herald              2
Sandy               2
Cupcake             2
Lorenzo             2
Penelope            2
Nugget              1
Joey                1
old                 1
Blipson             1
Kenny               1
Snickers            1
Evy                 1
Kane                1
Tuck                1
Geoff               1
Akumi               1
Reptar              1
Kendall             1
Aubie               1
Clybe               1
Jarod               1
Sage                1
Fillup              1
Teddy               1
Goliath             1
Kanu                1
Seamus              1
Angel               1
Todo                1
Zeus                1
Grady               1
Andy                1
Tupawc              1
Divine              1
Shakespeare         1
Tom                 1
Maya                1
Lenox               1
Wafer               1
Suki                1
Edd                 1
Tommy               1
Chadrick            1
Pupcasso            1
all                 1
Grizzwald           1
Ivar                1
Crumpet             1
Marq                1
Jennifur            1
Iggy                1
Rubio               1
Rose                1
Kuyu                1
Pancake             1
Ebby                1
Fynn                1
Astrid              1
his                 1
Dante               1
Julius              1
Dallas              1
Meera               1
Bubba               1
Bilbo               1
Bode                1
Godi                1
Mairi               1
Mollie              1
Combo               1
Dudley              1
Kellogg             1
Paull               1
Snoop               1
Carper              1
Scruffers           1
Rooney              1
Timber              1
Danny               1
Newt                1
Dale                1
Pluto               1
Stella              1
Beckham             1
Obi                 1
Jameson             1
Charl               1
infuriating         1
Kaiya               1
Sid                 1
Quinn               1
Thor                1
Adele               1
Maude               1
Emmie               1
Birf                1
Ralphus             1
Storkson            1
Hubertson           1
Blakely             1
Hall                1
Pawnd               1
Christoper          1
Ronnie              1
Ridley              1
Ike                 1
Sierra              1
Remy                1
Poppy               1
Charleson           1
Mason               1
Shadoe              1
Snoopy              1
Butter              1
Link                1
Frönq               1
Gunner              1
Chubbs              1
Mack                1
officially          1
Arnold              1
Ember               1
Kayla               1
Kramer              1
Lucia               1
Anakin              1
Aja                 1
Ferg                1
Mac                 1
Flurpson            1
Cermet              1
Hamrick             1
Eve                 1
Beya                1
Kathmandu           1
Kial                1
Jazz                1
Florence            1
Steve               1
Glenn               1
Cheesy              1
Tito                1
Chef                1
Hero                1
Willie              1
Izzy                1
Cannon              1
Arya                1
Tonks               1
Sunshine            1
Kloey               1
Dex                 1
Klein               1
Timison             1
Zooey               1
Tedders             1
Ralphé              1
Boston              1
Monty               1
Tanner              1
Kobe                1
Humphrey            1
Jeb                 1
Jaspers             1
Crimson             1
Millie              1
Halo                1
Opie                1
Nigel               1
Lillie              1
Lance               1
incredibly          1
Eevee               1
Lulu                1
Murphy              1
Godzilla            1
Rufio               1
Fabio               1
Geno                1
Trevith             1
Lassie              1
Timofy              1
Bert                1
Rey                 1
Yukon               1
Tilly               1
Chase               1
Lugan               1
Strudel             1
Gustav              1
Marlee              1
Fiji                1
Bobble              1
Rizzo               1
light               1
Wishes              1
Eriq                1
JD                  1
Kody                1
Jackie              1
Kulet               1
Crawford            1
Brandy              1
Cal                 1
Emma                1
Peanut              1
BeBe                1
Rontu               1
Bruiser             1
Keet                1
Lizzie              1
Bonaparte           1
Ito                 1
Zeek                1
Flash               1
Bronte              1
Tyrus               1
Ben                 1
Hercules            1
Gilbert             1
Sweet               1
Alexanderson        1
Sonny               1
Ace                 1
Coleman             1
Dutch               1
Jarvis              1
Siba                1
Trip                1
Miguel              1
Clifford            1
General             1
Jed                 1
Lorelei             1
Swagger             1
Clyde               1
Schnozz             1
Blu                 1
Cupid               1
Jeffri              1
this                1
Amélie              1
Simba               1
William             1
Corey               1
Herb                1
Jo                  1
Edmund              1
Trigger             1
Jimbo               1
Eugene              1
Rodman              1
Rumble              1
Jett                1
DayZ                1
by                  1
Vinnie              1
Saydee              1
Vinscent            1
Aqua                1
Rilo                1
Noosh               1
Molly               1
Mabel               1
Ralf                1
Karl                1
Michelangelope      1
Tug                 1
Major               1
Taco                1
Karll               1
Spark               1
Mark                1
Pete                1
Aiden               1
Alfy                1
Duddles             1
Kota                1
Sparky              1
Rumpole             1
Willy               1
Maxwell             1
Beemo               1
Nico                1
Nollie              1
Striker             1
Levi                1
Jim                 1
Venti               1
Liam                1
Blue                1
Margo               1
Cali                1
Willem              1
Oreo                1
Dawn                1
Gidget              1
Burt                1
Baloo               1
Jomathan            1
Jessiga             1
Boots               1
Amy                 1
Claude              1
Skye                1
Superpup            1
Ralph               1
Jebberson           1
Mookie              1
Pilot               1
Loomis              1
Shelby              1
Juckson             1
Pubert              1
Blanket             1
Jonah               1
Rupert              1
Jay                 1
Rodney              1
Glacier             1
Lilli               1
River               1
Mingus              1
Berb                1
Banjo               1
Stu                 1
Leonidas            1
Finnegus            1
Skittles            1
Ambrose             1
Ginger              1
Harper              1
Rambo               1
Naphaniel           1
Koko                1
Cedrick             1
Yoda                1
Chaz                1
Donny               1
Kaia                1
Aldrick             1
Dixie               1
Sailer              1
Rueben              1
Enchilada           1
Harvey              1
Bloop               1
Roscoe              1
Ralphy              1
Kingsley            1
Durg                1
Tove                1
Jeffrie             1
Marvin              1
Mattie              1
Karma               1
Benny               1
Brat                1
Howie               1
Chevy               1
Barclay             1
Brandonald          1
Carll               1
Balto               1
Harry               1
Buddah              1
Sephie              1
Chesterson          1
Arlen               1
Comet               1
Filup               1
Diogi               1
Bertson             1
Rizzy               1
Stubert             1
Darla               1
life                1
Franq               1
Tater               1
Travis              1
Einstein            1
Staniel             1
Fizz                1
Huxley              1
Daniel              1
Ken                 1
Taz                 1
Fletcher            1
Herschel            1
Lupe                1
Goose               1
Brutus              1
Laika               1
Hermione            1
Hurley              1
Ralpher             1
Meatball            1
Sailor              1
Pherb               1
Baron               1
Grey                1
Chuck               1
Orion               1
Augie               1
Canela              1
Creg                1
Mimosa              1
Lambeau             1
Lilah               1
Brandi              1
mad                 1
Mary                1
Severus             1
Grizzie             1
Jamesy              1
Vince               1
Rascal              1
Shiloh              1
Erik                1
Malikai             1
Damon               1
Griswold            1
Ricky               1
Rover               1
Rolf                1
Alejandro           1
Covach              1
Anna                1
Billl               1
Bookstore           1
Stewie              1
Rhino               1
Dewey               1
Oddie               1
Tedrick             1
Horace              1
Raphael             1
Dylan               1
Zuzu                1
Stefan              1
Perry               1
Tess                1
Mike                1
Skittle             1
Pepper              1
Happy               1
Dug                 1
Cleopatricia        1
Laela               1
Coopson             1
my                  1
Apollo              1
Darrel              1
Tassy               1
Cuddles             1
Gerbald             1
Spencer             1
Jareld              1
Derby               1
Bodie               1
Ralphson            1
Noah                1
space               1
Harrison            1
Ole                 1
Socks               1
Snicku              1
Cora                1
Julio               1
Acro                1
Ruffles             1
Dwight              1
Beebop              1
Olaf                1
Bauer               1
Tiger               1
Brudge              1
Dotsy               1
Gòrdón              1
Bruno               1
Gin                 1
Dook                1
Grizz               1
Callie              1
Maks                1
Sky                 1
Wesley              1
Hanz                1
Harlso              1
Ronduh              1
Mutt                1
Ron                 1
Pip                 1
Monkey              1
Iroh                1
Rudy                1
Sweets              1
Dobby               1
Joshwa              1
Harnold             1
Barry               1
Bungalo             1
Richie              1
Gordon              1
Walker              1
Zoe                 1
Chuq                1
Lolo                1
Sandra              1
Sprout              1
Mauve               1
Duchess             1
Fido                1
Jersey              1
Cheryl              1
Leela               1
Stephan             1
Rinna               1
Doobert             1
Patch               1
Terrance            1
Chelsea             1
Pippin              1
Ed                  1
Jaycob              1
Logan               1
Norman              1
Alexander           1
Torque              1
Freddery            1
Tessa               1
Bobbay              1
Jiminus             1
Obie                1
Ashleigh            1
Alf                 1
Wiggles             1
Ester               1
Ziva                1
Lacy                1
Marty               1
Hector              1
Kona                1
Barney              1
Stuart              1
Monster             1
Jerome              1
DonDon              1
Arlo                1
Mojo                1
Chompsky            1
Katie               1
Kallie              1
Pavlov              1
Brian               1
Schnitzel           1
O                   1
Spanky              1
Napolean            1
Puff                1
Moofasa             1
Deacon              1
Brownie             1
Kyro                1
Strider             1
Stormy              1
Lipton              1
Dunkin              1
Jessifer            1
Scott               1
Autumn              1
Kevon               1
Luther              1
Odin                1
Georgie             1
Andru               1
Tobi                1
Shawwn              1
Bowie               1
Dot                 1
Pinot               1
Philbert            1
Zara                1
Bobb                1
Dido                1
Emanuel             1
Fwed                1
Banditt             1
Carly               1
Terrenth            1
Edgar               1
Maisey              1
Eazy                1
Champ               1
Samsom              1
Jangle              1
Clarkus             1
Rorie               1
Bloo                1
Sprinkles           1
Jockson             1
Brockly             1
Mya                 1
Holly               1
Tayzie              1
Mona                1
Kollin              1
Toffee              1
Livvie              1
Bell                1
Kawhi               1
Pipsy               1
Chesney             1
Billy               1
Asher               1
Betty               1
Linus               1
Eleanor             1
Reagan              1
Shnuggles           1
Sobe                1
Tango               1
Tuco                1
Gromit              1
Heinrich            1
Vixen               1
Bradlay             1
Kenzie              1
Butters             1
Theo                1
Brooks              1
Bayley              1
Robin               1
Lili                1
Mosby               1
Emmy                1
such                1
Randall             1
Shooter             1
Nida                1
Ulysses             1
Traviss             1
Willow              1
Craig               1
Petrick             1
Nimbus              1
Pablo               1
Bobby               1
Winifred            1
Amber               1
Carbon              1
Darby               1
Stark               1
Huck                1
Henry               1
Mitch               1
Berkeley            1
Jeremy              1
Bluebert            1
Stephanus           1
Tino                1
Carter              1
Bradley             1
Gustaf              1
Clarq               1
Antony              1
Miley               1
Bones               1
Lilly               1
Caryl               1
Shaggy              1
Josep               1
Colin               1
Sundance            1
Tyrone              1
Sully               1
Remus               1
Anthony             1
Tycho               1
Sojourner           1
Ozzie               1
Jazzy               1
Philippe            1
Tebow               1
Kara                1
Pumpkin             1
Ralphie             1
Jordy               1
Moreton             1
Leonard             1
Longfellow          1
Meyer               1
Milky               1
Brady               1
Binky               1
Batdog              1
Lucky               1
Buckley             1
Timmy               1
Cecil               1
Furzey              1
Mo                  1
Tripp               1
Al                  1
Davey               1
Dietrich            1
Kirk                1
Chloe               1
Smiley              1
Crouton             1
Sora                1
Gert                1
Genevieve           1
unacceptable        1
Farfle              1
Hazel               1
Devón               1
Biden               1
Shikha              1
Cilantro            1
Name: name, dtype: int64

archive_clean.name[archive_clean.name.str.islower().fillna(False)]

22              such
56                 a
169            quite
193            quite
335              not
369              one
542       incredibly
649                a
801                a
819             very
852               my
924              one
988              not
992              his
993              one
1002               a
1004               a
1017               a
1025              an
1031            very
1040        actually
1049               a
1063            just
1071         getting
1095             mad
1097            very
1120            this
1121    unacceptable
1138             all
1193               a
1206             old
1207               a
1259     infuriating
1340               a
1351               a
1361               a
1362              an
1368               a
1382               a
1385            very
1435         getting
1457            just
1499               a
1527             the
1603             the
1693        actually
1724              by
1737               a
1747      officially
1785               a
1797             the
1815             the
1853               a
1854               a
1877               a
1878               a
1916            life
1923               a
1936             one
1941               a
1955               a
1994               a
2001           light
2019            just
2030           space
2034               a
2037             the
2066               a
2116               a
2125               a
2128               a
2146               a
2153               a
2161               a
2191               a
2198               a
2204              an
2211               a
2212             the
2218               a
2222               a
2235               a
2249               a
2255               a
2264               a
2273               a
2287               a
2304               a
2311               a
2314               a
2326           quite
2327               a
2333              an
2334               a
2335              an
2345             the
2346             the
2347               a
2348               a
2349              an
2350               a
2352               a
2353               a
2354               a
Name: name, dtype: object

Lower names seems to be the ones incorrect, will replace with none values

archive_clean.name[archive_clean.name.str.islower().fillna(False)] = np.nan

test¶

archive_clean.name.value_counts(dropna=False)

NaN               726
Lucy               11
Charlie            11
Oliver             10
Cooper             10
Penny               9
Tucker              9
Lola                8
Winston             8
Sadie               8
Toby                7
Daisy               7
Bella               6
Jax                 6
Oscar               6
Stanley             6
Koda                6
Bo                  6
Bailey              6
Louis               5
Chester             5
Rusty               5
Dave                5
Buddy               5
Bentley             5
Leo                 5
Scout               5
Milo                5
Sophie              4
Archie              4
Larry               4
Alfie               4
Jack                4
Dexter              4
Scooter             4
Gary                4
Phil                4
Bear                4
Finn                4
Chip                4
Maggie              4
Duke                4
Clark               4
Cassie              4
Clarence            4
Jerry               4
Sammy               4
Oakley              4
Gus                 4
Derek               4
Winnie              4
Reggie              4
Jeffrey             4
Brody               4
George              4
Boomer              4
Rosie               3
Loki                3
Otis                3
Olive               3
Wallace             3
Zoey                3
Hank                3
Gizmo               3
Max                 3
Beau                3
Riley               3
Bruce               3
Sunny               3
Ellie               3
Ted                 3
Reginald            3
Nala                3
Malcolm             3
Klevin              3
Louie               3
Sebastian           3
Moose               3
Luna                3
Carl                3
Zeke                3
Samson              3
Waffles             3
Kyle                3
Steven              3
Wilson              3
Wyatt               3
Gerald              3
Jimothy             3
Ruby                3
Walter              3
Lily                3
Calvin              3
Mia                 3
Shadow              3
Vincent             3
Doug                3
Maximus             3
Reese               3
Earl                3
Rocky               2
Bubbles             2
Colby               2
Jeph                2
Kreg                2
Atticus             2
Arnie               2
Chipson             2
Bob                 2
Kreggory            2
Herm                2
Alice               2
Churlie             2
Atlas               2
Crystal             2
Luca                2
Cody                2
Pippa               2
Lincoln             2
Mister              2
Juno                2
Albert              2
Nelly               2
Sam                 2
Solomon             2
Cash                2
Jimison             2
Keith               2
Aspen               2
Griffin             2
Piper               2
Theodore            2
Fred                2
Sansa               2
Bernie              2
Peaches             2
Franklin            2
Flávio              2
Kilo                2
CeCe                2
Rocco               2
Kenneth             2
Blitz               2
Kirby               2
Belle               2
Fiona               2
Gabe                2
Raymond             2
Ash                 2
Jesse               2
Indie               2
Brad                2
Lennon              2
Penelope            2
Linda               2
Harold              2
Herald              2
Marley              2
Dash                2
Keurig              2
Roosevelt           2
Pickles             2
Patrick             2
Benedict            2
Watson              2
Jackson             2
Benji               2
Abby                2
Lenny               2
Olivia              2
Oshie               2
Neptune             2
Tyr                 2
Hunter              2
Sarge               2
Ollie               2
Titan               2
Jiminy              2
Finley              2
Paisley             2
Terry               2
Thumas              2
Odie                2
Wally               2
Moe                 2
Albus               2
Charles             2
Lou                 2
Yogi                2
Doc                 2
Opal                2
Misty               2
Chuckles            2
Coco                2
Smokey              2
Rufus               2
Trooper             2
Coops               2
Hammond             2
Dakota              2
Baxter              2
Oliviér             2
Frank               2
Bisquick            2
Romeo               2
Phineas             2
Layla               2
Calbert             2
Curtis              2
Maxaroni            2
Ozzy                2
Elliot              2
Eli                 2
Percy               2
Django              2
Kevin               2
Rory                2
Lorenzo             2
Frankie             2
Sugar               2
Sampson             2
Axel                2
Ava                 2
Cupcake             2
Chet                2
Panda               2
Remington           2
Phred               2
Maddie              2
Sandy               2
Hobbes              2
Eve                 1
Trip                1
Pluto               1
Obi                 1
Miguel              1
Clifford            1
Stella              1
General             1
Jed                 1
Blakely             1
Ace                 1
Hall                1
Strudel             1
Keet                1
Hubertson           1
Lizzie              1
Evy                 1
Sierra              1
Kial                1
Bonaparte           1
Kaiya               1
Christoper          1
Ralphus             1
Birf                1
Maude               1
Adele               1
Quinn               1
Sid                 1
Scruffers           1
Mac                 1
Lucia               1
Hercules            1
Goliath             1
Seamus              1
Angel               1
Alexanderson        1
Sweet               1
Andy                1
Gilbert             1
Jarod               1
Combo               1
Tuck                1
Ben                 1
Blipson             1
Bode                1
Geoff               1
Reptar              1
Tyrus               1
Ito                 1
Sonny               1
Jarvis              1
Fillup              1
Dutch               1
Kathmandu           1
Mack                1
Cermet              1
Flurpson            1
Newt                1
Anakin              1
Kramer              1
Coleman             1
Kayla               1
Chubbs              1
Butter              1
Snoopy              1
Shadoe              1
Poppy               1
Dudley              1
Gustav              1
Bert                1
Marlee              1
Shakespeare         1
Tupawc              1
Peanut              1
Sage                1
Kanu                1
Kellogg             1
Emma                1
Cal                 1
Remy                1
Charleson           1
Link                1
Brandy              1
Crawford            1
Kulet               1
Ferg                1
Jackie              1
Hamrick             1
Todo                1
Divine              1
Tom                 1
JD                  1
Clybe               1
Bruiser             1
Clarkus             1
Rorie               1
Alexander           1
Frönq               1
Naphaniel           1
Cupid               1
Harper              1
Chadrick            1
Ivar                1
Pancake             1
Bubba               1
Wafer               1
BeBe                1
Kendall             1
Kane                1
Nugget              1
Carper              1
Rooney              1
Danny               1
Rubio               1
Suki                1
Zeus                1
Akumi               1
Snickers            1
Beya                1
Grady               1
Lilly               1
Rizzo               1
Mairi               1
Gunner              1
Ember               1
Jameson             1
Ronnie              1
Darrel              1
Bobble              1
Fiji                1
Timber              1
Kuyu                1
Iggy                1
Eriq                1
Grizzwald           1
Dale                1
Charl               1
Thor                1
Emmie               1
Storkson            1
Beckham             1
Wishes              1
Snoop               1
Ike                 1
Mason               1
Ridley              1
Teddy               1
Aubie               1
Crumpet             1
Arnold              1
Aja                 1
Kody                1
Maya                1
Bronte              1
Mollie              1
Finnegus            1
Ambrose             1
Ginger              1
Rontu               1
Pilot               1
Mookie              1
Jebberson           1
Cannon              1
Levi                1
Izzy                1
Willie              1
Jim                 1
Venti               1
Liam                1
Blue                1
Margo               1
Cali                1
Willem              1
Skittles            1
Arya                1
Dawn                1
Leonidas            1
Klein               1
Pubert              1
Blanket             1
Dex                 1
Rupert              1
Kloey               1
Jay                 1
Rodney              1
Glacier             1
Loomis              1
Sunshine            1
Tonks               1
Lilli               1
Mingus              1
Berb                1
Banjo               1
Stu                 1
Oreo                1
Gidget              1
Lenox               1
Halo                1
Nigel               1
Lillie              1
Glenn               1
Lance               1
Kobe                1
Humphrey            1
Eevee               1
Timofy              1
Jaspers             1
Lulu                1
Godzilla            1
Rufio               1
Murphy              1
Fabio               1
Geno                1
Trevith             1
Lassie              1
Opie                1
Millie              1
Burt                1
Crimson             1
Baloo               1
Jomathan            1
Jessiga             1
Boots               1
Amy                 1
Claude              1
Hero                1
Chef                1
Skye                1
Superpup            1
Tito                1
Ralph               1
Nollie              1
Swagger             1
Chase               1
Lorelei             1
Cheesy              1
Timison             1
Zooey               1
Juckson             1
Florence            1
Clyde               1
Tilly               1
Molly               1
Mabel               1
Steve               1
Ralf                1
Karl                1
Yukon               1
Michelangelope      1
Tug                 1
Major               1
Tanner              1
Taco                1
Karll               1
Monty               1
Noosh               1
Spark               1
Lugan               1
Pawnd               1
Shelby              1
Zeek                1
Godi                1
Dallas              1
Bilbo               1
Meera               1
Rey                 1
Julius              1
Dante               1
Astrid              1
Fynn                1
Ebby                1
Flash               1
Rose                1
Jennifur            1
Marq                1
Pupcasso            1
Tommy               1
Edd                 1
Pete                1
Aiden               1
Alfy                1
Duddles             1
Edmund              1
Trigger             1
Jimbo               1
Eugene              1
Rodman              1
Rumble              1
Jett                1
Ralphé              1
DayZ                1
River               1
Vinnie              1
Saydee              1
Beemo               1
Schnozz             1
Nico                1
Striker             1
Tedders             1
Jo                  1
Herb                1
Boston              1
Aqua                1
Kota                1
Sparky              1
Rumpole             1
Willy               1
Maxwell             1
Mark                1
Rilo                1
Vinscent            1
Corey               1
Blu                 1
Jangle              1
Jazz                1
Jeffri              1
Amélie              1
Simba               1
William             1
Tess                1
Pipsy               1
Joey                1
Enchilada           1
Yoda                1
Chaz                1
Donny               1
Kaia                1
Severus             1
Aldrick             1
Dixie               1
Sailer              1
Rueben              1
Harvey              1
Benny               1
Bloop               1
Roscoe              1
Ralphy              1
Kingsley            1
Durg                1
Tove                1
Jeffrie             1
Grizzie             1
Mattie              1
Cedrick             1
Brat                1
Marvin              1
Comet               1
Barclay             1
Brandonald          1
Carll               1
Balto               1
Harry               1
Sephie              1
Chesterson          1
Arlen               1
Chevy               1
Diogi               1
Howie               1
Rizzy               1
Stubert             1
Darla               1
Butters             1
Franq               1
Tater               1
Staniel             1
Travis              1
Filup               1
Rascal              1
Karma               1
Einstein            1
Brutus              1
Hurley              1
Canela              1
Daniel              1
Ken                 1
Taz                 1
Fletcher            1
Herschel            1
Lupe                1
Goose               1
Laika               1
Damon               1
Huxley              1
Hermione            1
Bradley             1
Antony              1
Sailor              1
Pherb               1
Baron               1
Grey                1
Chuck               1
Creg                1
Henry               1
Anna                1
Covach              1
Fizz                1
Mimosa              1
Lilah               1
Brandi              1
Mary                1
Rolf                1
Erik                1
Jamesy              1
Vince               1
Ralpher             1
Lambeau             1
Shiloh              1
Malikai             1
Meatball            1
Griswold            1
Ricky               1
Rover               1
Augie               1
Alejandro           1
Billl               1
Bookstore           1
Miley               1
Zuzu                1
Rinna               1
Cuddles             1
Tassy               1
Dewey               1
Oddie               1
Ronduh              1
Horace              1
Raphael             1
Gin                 1
Stefan              1
Brudge              1
Perry               1
Mike                1
Skittle             1
Pepper              1
Happy               1
Dug                 1
Cleopatricia        1
Laela               1
Coopson             1
Cora                1
Tiger               1
Rhino               1
Harrison            1
Tedrick             1
Gerbald             1
Jareld              1
Mauve               1
Walker              1
Derby               1
Bodie               1
Ralphson            1
Noah                1
Ole                 1
Bauer               1
Socks               1
Spencer             1
Snicku              1
Julio               1
Acro                1
Ruffles             1
Dwight              1
Beebop              1
Olaf                1
Apollo              1
Colin               1
Dotsy               1
Harlso              1
Ron                 1
Bungalo             1
Dook                1
Grizz               1
Callie              1
Maks                1
Sky                 1
Wesley              1
Hanz                1
Mutt                1
Gòrdón              1
Pip                 1
Monkey              1
Iroh                1
Rudy                1
Sweets              1
Dobby               1
Joshwa              1
Harnold             1
Barry               1
Richie              1
Leela               1
Jaycob              1
Ed                  1
Bruno               1
Gordon              1
Zoe                 1
Chuq                1
Lolo                1
Sandra              1
Sprout              1
Koko                1
Duchess             1
Fido                1
Jersey              1
Bertson             1
Cheryl              1
Stephan             1
Doobert             1
Patch               1
Terrance            1
Chelsea             1
Pippin              1
Orion               1
Logan               1
Champ               1
Livvie              1
Katie               1
Chompsky            1
Freddery            1
Tessa               1
Bobbay              1
Kenny               1
Obie                1
Jiminus             1
Alf                 1
Buddah              1
Strider             1
Ziva                1
Lacy                1
Dido                1
Hector              1
Kona                1
Barney              1
Emanuel             1
Stuart              1
Monster             1
Kallie              1
Odin                1
Binky               1
Moofasa             1
Gromit              1
Torque              1
Tuco                1
Brian               1
Schnitzel           1
O                   1
Spanky              1
Napolean            1
Puff                1
Deacon              1
Cilantro            1
Brownie             1
Pavlov              1
Kyro                1
Wiggles             1
Lipton              1
Ester               1
Jessifer            1
Marty               1
Kevon               1
Jerome              1
DonDon              1
Arlo                1
Philbert            1
Sobe                1
Tango               1
Chesney             1
Sprinkles           1
Carly               1
Bloo                1
Bowie               1
Dot                 1
Pinot               1
Zara                1
Mojo                1
Bobb                1
Samsom              1
Paull               1
Fwed                1
Shawwn              1
Banditt             1
Edgar               1
Maisey              1
Eazy                1
Shnuggles           1
Reagan              1
Eleanor             1
Linus               1
Georgie             1
Andru               1
Tobi                1
Jockson             1
Mya                 1
Holly               1
Tayzie              1
Mona                1
Kollin              1
Toffee              1
Terrenth            1
Bell                1
Kawhi               1
Brockly             1
Dylan               1
Billy               1
Asher               1
Betty               1
Rambo               1
Vixen               1
Brady               1
Stewie              1
Mosby               1
Lucky               1
Bradlay             1
Kenzie              1
Ashleigh            1
Theo                1
Brooks              1
Bayley              1
Robin               1
Lili                1
Emmy                1
Winifred            1
Randall             1
Shooter             1
Nida                1
Ulysses             1
Traviss             1
Willow              1
Craig               1
Petrick             1
Nimbus              1
Bobby               1
Amber               1
Milky               1
Bluebert            1
Norman              1
Heinrich            1
Darby               1
Stark               1
Huck                1
Siba                1
Mitch               1
Berkeley            1
Jeremy              1
Stephanus           1
Carter              1
Carbon              1
Tino                1
Gustaf              1
Clarq               1
Biden               1
Bones               1
Jonah               1
Caryl               1
Shaggy              1
Pablo               1
Josep               1
Sundance            1
Jazzy               1
Pumpkin             1
Batdog              1
Sully               1
Remus               1
Anthony             1
Tycho               1
Sojourner           1
Luther              1
Ozzie               1
Philippe            1
Buckley             1
Jeb                 1
Tyrone              1
Kara                1
Ralphie             1
Jordy               1
Moreton             1
Leonard             1
Longfellow          1
Meyer               1
Scott               1
Crouton             1
Shikha              1
Dunkin              1
Cecil               1
Furzey              1
Mo                  1
Tripp               1
Al                  1
Davey               1
Tebow               1
Dietrich            1
Kirk                1
Chloe               1
Timmy               1
Smiley              1
Stormy              1
Sora                1
Gert                1
Genevieve           1
Farfle              1
Hazel               1
Devón               1
Autumn              1
Name: name, dtype: int64

archive_clean.name[archive_clean.name.str.islower().fillna(False)]

Series([], Name: name, dtype: object)

Define¶

Several_ids columns need to be dropped and also retweed columns details.
retweeted information also should be removed.

code¶

archive_clean.columns

Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],
      dtype='object')

archive_clean.drop(['in_reply_to_status_id', 'in_reply_to_user_id', 'retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp'],\
                   axis=1, inplace=True)

test¶

archive_clean.columns

Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
       'rating_numerator', 'rating_denominator', 'name', 'doggo', 'floofer',
       'pupper', 'puppo'],
      dtype='object')

archive_clean.shape

(2117, 12)

Define¶

wrong numerator and denominator ratings.(get correct values from tweets)

archive_clean['rating_numerator'].min()

0

code¶

x = archive_clean['text'][516]
x

'Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \nKeep Sam smiling by clicking and sharing this link:\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx'

import re
temp = re.findall(r'\d+/\d+', x)
temp

['24/7']

# regex = re.compile(r'\d+/\d')
# #archive_clean['text_ratings'] = archive_clean['text'].apply(
#                 lambda x : regex.findall(x))

archive_clean['text_ratings'] = archive_clean['text'].str.extract(r'(\d+/\d+)')
archive_clean['text_ratings'].sample(20)

1907    10/10
1139    11/10
1051    11/10
1275    13/10
809     12/10
947     11/10
1472    10/10
1353    10/10
2085    10/10
572     10/10
816     10/10
1344    12/10
1452    13/10
1454    12/10
1107    11/10
1450    11/10
2257    11/10
339     14/10
1965     7/10
1842    11/10
Name: text_ratings, dtype: object

archive_clean['text_ratings'].str.split('/', expand=True)

archive_clean[['rating_numerator', 'rating_denominator']] = archive_clean['text_ratings'].str.split('/', expand=True)

archive_clean[['rating_numerator', 'rating_denominator']] = archive_clean[['rating_numerator', 'rating_denominator']].astype(int)

archive_clean.drop('text_ratings', axis=1, inplace=True)

test¶

archive_clean[['rating_numerator', 'rating_denominator']].describe()

archive_clean['rating_numerator'].idxmax()

979

archive_clean['text'][979]

"This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh"

archive_clean['rating_denominator'].idxmax()

1120

archive_clean['text'][1120]

'Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv'

Tidiness¶

Define¶

Dog stages is an observation should be put into a column

archive_clean.columns

Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
       'rating_numerator', 'rating_denominator', 'name', 'doggo', 'floofer',
       'pupper', 'puppo'],
      dtype='object')

code¶

archive_clean['dog_stage'] = archive_clean[['doggo', 'floofer', 'pupper', 'puppo']].replace(np.nan, '').sum(1)

archive_clean['dog_stage'] = archive_clean['dog_stage'].replace('', np.nan)

archive_clean.drop(['doggo', 'floofer', 'pupper', 'puppo'], axis=1, inplace=True)

test¶

archive_clean['dog_stage'].value_counts(dropna=False)

NaN             1779
pupper           222
doggo             72
puppo             23
doggopupper       10
floofer            9
doggofloofer       1
doggopuppo         1
Name: dog_stage, dtype: int64

archive_clean.head()

Define¶

api dataframe should be merged to archive dataframe to have 1 dataframe(each type of observation forms a table)

code¶

api_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2327 entries, 0 to 2326
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   tweet_id        2327 non-null   object
 1   retweet_count   2327 non-null   int64 
 2   favorite_count  2327 non-null   int64 
dtypes: int64(2), object(1)
memory usage: 54.7+ KB

to merge convert archive_clean tweet_id to object data type)

archive_clean['tweet_id'] = archive_clean['tweet_id'].apply(str)

df = archive_clean.merge(api_df, how='inner', on='tweet_id')

test¶

df.head()

df.shape

(2109, 11)

Define¶

only take the true predicted image, with high confidence

image.head()

# prediction order were in order of confidence of model prediction
for index,row in image_clean.iterrows():
    if row['p1_dog'] == True:
        image_clean.at[index,'prediction_conf'] = row['p1_conf']
        image_clean.at[index,'dog_breed_pred'] = row['p1']
        
    elif row['p2_dog'] == True:
        image_clean.at[index,'prediction_conf'] = row['p2_conf']
        image_clean.at[index,'dog_breed_pred'] = row['p2']
    
    elif row['p3_dog'] == True:
        image_clean.at[index,'prediction_conf'] = row['p3_conf']
        image_clean.at[index,'dog_breed_pred'] = row['p3']

image_clean = image_clean[['tweet_id', 'jpg_url','img_num', 'dog_breed_pred', 'prediction_conf']]

Define¶

merge image data into former merged dataframe

code¶

image_clean.shape

(2075, 5)

image_clean['tweet_id'] = image_clean['tweet_id'].apply(str)

final_df = df.merge(image_clean, how='inner', on='tweet_id')

test¶

final_df.head()

final_df.shape

(1986, 15)

Storing clean data¶

final_df.to_csv('twitter_archive_master.csv', index=False)

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True

	count	mean	std	min	25%	50%	75%	max
rating_numerator	2356.0	13.126486	45.876648	0.0	10.0	11.0	12.0	1776.0
rating_denominator	2356.0	10.455433	6.745237	0.0	10.0	10.0	10.0	170.0

	tweet_id	img_num	p1_conf	p2_conf	p3_conf
count	2.075000e+03	2075.000000	2075.000000	2.075000e+03	2.075000e+03
mean	7.384514e+17	1.203855	0.594548	1.345886e-01	6.032417e-02
std	6.785203e+16	0.561875	0.271174	1.006657e-01	5.090593e-02
min	6.660209e+17	1.000000	0.044333	1.011300e-08	1.740170e-10
25%	6.764835e+17	1.000000	0.364412	5.388625e-02	1.622240e-02
50%	7.119988e+17	1.000000	0.588230	1.181810e-01	4.944380e-02
75%	7.932034e+17	1.000000	0.843855	1.955655e-01	9.180755e-02
max	8.924206e+17	4.000000	1.000000	4.880140e-01	2.734190e-01

	rating_numerator	rating_denominator
count	2117.000000	2117.000000
mean	12.246103	10.501181
std	40.275298	7.105845
min	0.000000	2.000000
25%	10.000000	10.000000
50%	11.000000	10.000000
75%	12.000000	10.000000
max	1776.000000	170.000000

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
0	892420643555336193	NaN	NaN	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	None	None	None	None
1	892177421306343426	NaN	NaN	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	None	None	None	None
2	891815181378084864	NaN	NaN	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	None	None	None	None
3	891689557279858688	NaN	NaN	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	None	None	None	None
4	891327558926688256	NaN	NaN	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	None	None	None	None

	tweet_id	retweet_count	favorite_count
0	892420643555336193	7001	33801
1	892177421306343426	5299	29301
2	891815181378084864	3477	22042
3	891689557279858688	7223	36920
4	891327558926688256	7754	35299

	0	1
0	13	10
1	13	10
2	12	10
3	13	10
4	12	10
5	13	10
6	13	10
7	13	10
8	13	10
9	14	10
10	13	10
11	13	10
12	13	10
13	12	10
14	13	10
15	13	10
16	12	10
17	13	10
18	13	10
20	12	10
21	13	10
22	14	10
23	13	10
24	13	10
25	12	10
26	13	10
27	13	10
28	13	10
29	12	10
31	13	10
33	12	10
34	13	10
35	14	10
37	13	10
38	12	10
39	13	10
40	13	10
41	14	10
42	13	10
43	12	10
44	12	10
45	5	10
46	13	10
47	13	10
48	13	10
49	12	10
50	13	10
51	13	10
52	13	10
53	12	10
54	13	10
56	14	10
57	12	10
58	13	10
59	12	10
60	12	10
61	12	10
62	11	10
63	13	10
65	12	10
66	13	10
67	12	10
69	11	10
70	13	10
71	13	10
72	13	10
75	13	10
76	14	10
77	13	10
79	12	10
80	13	10
81	12	10
82	12	10
83	14	10
84	13	10
85	13	10
86	13	10
87	13	10
88	13	10
89	12	10
90	12	10
92	13	10
93	12	10
94	13	10
96	13	10
98	12	10
99	12	10
100	13	10
102	13	10
103	12	10
104	13	10
105	12	10
106	13	10
107	12	10
108	12	10
110	14	10
111	13	10
112	11	10
114	13	10
115	13	10
116	13	10
117	14	10
119	13	10
120	13	10
121	12	10
122	13	10
123	14	10
125	13	10
126	12	10
127	12	10
128	13	10
129	12	10
131	13	10
133	13	10
134	12	10
135	13	10
136	12	10
138	13	10
139	13	10
140	13	10
141	14	10
142	12	10
143	13	10
144	13	10
145	13	10
147	12	10
149	14	10
150	13	10
151	13	10
152	11	10
153	13	10
154	13	10
156	13	10
157	13	10
158	12	10
161	13	10
162	13	10
163	12	10
164	14	10
166	12	10
167	13	10
168	13	10
169	12	10
170	13	10
172	13	10
173	13	10
174	12	10
175	12	10
176	13	10
177	13	10
178	13	10
181	12	10
183	12	10
184	14	10
187	14	10
190	13	10
191	13	10
192	13	10
193	12	10
196	12	10
197	13	10
198	13	10
199	14	10
200	11	10
201	12	10
202	13	10
203	13	10
205	12	10
206	12	10
207	13	10
208	13	10
209	14	10
210	12	10
213	11	10
214	14	10
215	13	10
216	11	10
217	13	10
219	11	10
220	12	10
221	12	10
223	12	10
224	11	10
225	13	10
226	12	10
227	12	10
229	6	10
232	10	10
233	12	10
235	12	10
236	12	10
237	11	10
238	11	10
239	13	10
240	13	10
241	11	10
242	13	10
243	13	10
244	12	10
245	13	10
246	12	10
248	13	10
249	12	10
251	13	10
252	12	10
253	13	10
254	11	10
255	12	10
256	13	10
257	12	10
258	11	10
259	13	10
261	13	10
262	12	10
263	13	10
264	12	10
265	12	10
267	12	10
268	14	10
269	12	10
270	13	10
271	12	10
275	10	10
276	12	10
277	13	10
278	12	10
279	13	10
280	12	10
282	13	10
283	13	10
284	13	10
287	13	10
288	13	10
292	13	10
293	12	10
294	13	10
295	12	10
296	13	10
297	13	10
299	12	10
300	12	10
301	11	10
304	12	10
305	13	10
306	13	10
308	11	10
311	12	10
312	12	10
314	12	10
315	0	10
316	12	10
317	13	10
318	13	10
320	12	10
321	12	10
322	11	10
323	10	10
324	12	10
325	12	10
326	13	10
328	13	10
329	13	10
330	12	10
331	13	10
333	13	10
334	13	10
335	10	10
336	12	10
337	12	10
338	11	10
339	14	10
344	12	10
345	12	10
347	14	10
348	13	10
349	13	10
350	13	10
351	12	10
352	13	10
353	12	10
354	12	10
355	12	10
356	13	10
358	13	10
360	12	10
361	12	10
362	11	10
363	13	10
364	12	10
365	13	10
367	13	10
368	10	10
369	14	10
370	12	10
371	12	10
372	14	10
373	13	10
374	12	10
376	13	10
377	11	10
378	12	10
379	12	10
380	13	10
381	12	10
383	13	10
384	12	10
385	13	10
388	13	10
389	12	10
390	11	10
391	13	10
392	13	10
393	11	10
394	12	10
395	14	10
396	13	10
398	12	10
400	12	10
401	13	10
402	13	10
403	11	10
404	14	10
405	12	10
407	12	10
408	11	10
410	11	10
412	12	10
413	13	10
414	12	10
416	12	10
417	13	10
418	14	10
419	11	10
421	13	10
423	13	10
424	13	10
426	14	10
428	12	10
429	11	10
430	12	10
432	13	10
433	84	70
436	14	10
437	12	10
439	13	10
440	11	10
441	11	10
442	13	10
443	12	10
444	10	10
445	12	10
448	14	10
449	14	10
451	13	10
452	12	10
454	11	10
456	12	10
457	11	10
458	12	10
459	13	10
460	13	10
461	12	10
463	13	10
464	11	10
466	13	10
467	13	10
468	11	10
470	12	10
471	12	10
472	13	10
473	11	10
474	12	10
477	11	10
478	13	10
480	11	10
481	12	10
482	13	10
483	11	10
484	12	10
486	12	10
487	12	10
489	11	10
490	14	10
491	12	10
492	13	10
493	11	10
494	13	10
495	12	10
496	12	10
497	13	10
499	11	10
500	12	10
501	11	10
502	11	10
503	12	10
504	13	10
505	10	10
507	12	10
508	13	10
509	12	10
510	13	10
511	11	10
512	13	10
514	10	10
515	11	10
516	24	7
517	11	10
518	11	10
519	13	10
520	12	10
521	10	10
523	12	10
524	12	10
525	11	10
526	13	10
527	12	10
528	12	10
529	11	10
531	12	10
532	12	10
533	14	10
534	13	10
536	12	10
537	13	10
539	13	10
540	11	10
542	11	10
544	12	10
545	12	10
547	13	10
548	12	10
549	12	10
550	12	10
551	13	10
553	13	10
554	12	10
556	13	10
557	12	10
559	11	10
560	12	10
562	11	10
563	12	10
564	13	10
565	11	10
567	12	10
569	12	10
571	12	10
572	10	10
573	13	10
575	12	10
578	12	10
579	11	10
580	12	10
582	12	10
584	13	10
585	11	10
587	13	10
588	12	10
590	11	10
591	11	10
592	12	10
593	11	10
607	13	10
608	12	10
609	12	10
610	12	10
613	12	10
614	11	10
616	12	10
617	12	10
619	11	10
620	13	10
621	11	10
622	12	10
623	13	10
624	13	10
625	12	10
626	11	10
628	12	10
630	12	10
631	14	10
632	12	10
633	13	10
635	12	10
636	12	10
637	13	10
638	12	10
639	12	10
640	13	10
641	11	10
642	10	10
643	12	10
644	11	10
645	12	10
646	11	10
647	13	10
648	11	10
649	13	10
650	12	10
651	12	10
652	12	10
653	11	10
657	14	10
658	13	10
659	12	10
660	12	10
662	12	10
663	12	10
665	12	10
666	11	10
667	12	10
668	11	10
670	12	10
672	13	10
673	13	10
674	12	10
675	12	10
676	13	10
678	10	10
679	12	10
680	11	10
681	12	10
683	11	10
684	13	10
685	13	10
687	12	10
688	11	10
689	13	10
690	11	10
691	10	10
693	12	10
695	75	10
696	12	10
697	11	10
698	13	10
699	12	10
700	11	10
703	12	10
704	12	10
705	10	10
706	11	10
708	12	10
709	11	10
710	13	10
711	11	10
712	12	10
713	12	10
714	12	10
715	13	10
716	12	10
717	12	10
718	11	10
719	13	10
721	12	10
722	11	10
723	12	10
724	11	10
725	10	10
726	11	10
727	12	10
729	11	10
730	5	10
731	11	10
732	12	10
733	12	10
734	11	10
735	10	10
736	13	10
737	10	10
738	11	10
739	12	10
740	12	10
743	11	10
744	11	10
746	10	10
747	11	10
748	12	10
750	12	10
751	13	10
752	11	10
754	13	10
755	11	10
756	10	10
757	11	10
758	14	10
760	13	10
761	11	10
762	12	10
763	27	10
765	3	10
766	12	10
768	12	10
769	12	10
771	12	10
772	11	10
774	13	10
775	10	10
776	11	10
777	10	10
779	12	10
780	11	10
781	13	10
782	11	10
783	12	10
785	13	10
786	12	10
787	10	10
788	14	10
789	11	10
790	12	10
791	12	10
792	10	10
793	11	10
795	12	10
796	10	10
797	12	10
798	11	10
799	11	10
801	10	10
802	12	10
803	10	10
804	12	10
805	10	10
806	11	10
807	13	10
808	11	10
809	12	10
810	11	10
812	11	10
813	12	10
814	7	10
816	10	10
817	11	10
819	11	10
820	11	10
821	10	10
823	10	10
824	12	10
825	13	10
827	12	10
828	12	10
830	11	10
831	10	10
832	12	10
834	11	10
835	8	10
836	10	10
837	11	10
838	12	10
839	13	10
840	13	10
842	10	10
844	11	10
845	9	10
846	12	10
848	12	10
849	11	10
850	12	10
851	12	10
852	13	10
853	11	10
854	10	10
855	12	10
856	10	10
858	11	10
859	8	10
861	10	10
862	11	10
863	12	10
864	12	10
865	11	10
866	14	10
867	12	10
869	11	10
870	10	10
871	11	10
873	10	10
874	11	10
875	10	10
876	11	10
877	11	10
878	11	10
879	12	10
880	12	10
881	12	10
882	13	10
883	4	10
884	10	10
886	12	10
887	10	10
888	11	10
889	12	10
891	10	10
892	11	10
893	10	10
894	12	10
896	7	10
897	11	10
898	11	10
899	13	10
900	12	10
901	11	10
902	165	150
903	10	10
904	11	10
905	13	10
906	9	10
907	10	10
909	11	10
910	12	10
912	4	10
913	13	10
914	10	10
915	8	10
916	11	10
917	12	10
918	11	10
919	13	10
920	11	10
921	12	10
922	10	10
923	12	10
924	14	10
925	13	10
927	11	10
928	12	10
929	10	10
930	11	10
931	10	10
932	12	10
933	10	10
934	11	10
935	12	10
936	8	10
938	11	10
939	13	10
940	10	10
941	12	10
942	11	10
944	10	10
945	10	10
946	9	10
947	11	10
948	8	10
950	12	10
951	13	10
952	11	10
953	12	10
954	11	10
955	13	10
956	5	10
957	10	10
958	12	10
959	10	10
960	12	10
961	10	10
962	11	10
963	12	10
964	8	10
965	12	10
966	9	10
968	13	10
969	11	10
970	10	10
971	11	10
972	12	10
973	10	10
974	11	10
975	13	10
976	10	10
977	11	10
978	12	10
979	1776	10
980	12	10
981	11	10
982	10	10
983	10	10
984	12	10
985	11	10
986	11	10
987	8	10
988	10	10
989	13	10
990	13	10
991	11	10
992	10	10
993	6	10
994	12	10
995	10	10
996	13	10
997	11	10
998	7	10
999	13	10
1000	10	10
1001	11	10
1002	8	10
1003	11	10
1004	4	10
1006	12	10
1007	10	10
1008	11	10
1009	10	10
1010	10	10
1011	12	10
1013	11	10
1014	10	10
1015	11	10
1016	0	10
1017	11	10
1018	13	10
1019	10	10
1020	11	10
1021	12	10
1022	11	10
1024	10	10
1025	9	10
1026	10	10
1027	11	10
1028	10	10
1029	7	10
1030	10	10
1031	9	10
1032	12	10
1033	11	10
1034	12	10
1035	9	10
1036	12	10
1037	10	10
1038	11	10
1039	13	10
1040	12	10
1041	11	10
1042	10	10
1044	10	10
1045	7	10
1046	11	10
1047	13	10
1048	10	10
1049	12	10
1050	10	10
1051	11	10
1052	10	10
1053	14	10
1054	12	10
1055	11	10
1056	12	10
1057	8	10
1058	10	10
1059	11	10
1060	9	10
1061	12	10
1062	11	10
1063	12	10
1064	10	10
1065	9	10
1066	8	10
1067	11	10
1068	9	11
1069	10	10
1070	9	10
1071	10	10
1072	12	10
1073	10	10
1074	12	10
1075	12	10
1076	9	10
1077	10	10
1078	6	10
1079	13	10
1081	11	10
1082	10	10
1083	11	10
1084	10	10
1085	9	10
1086	12	10
1087	10	10
1088	10	10
1089	10	10
1090	11	10
1091	13	10
1092	9	10
1093	13	10
1094	13	10
1095	13	10
1096	11	10
1097	10	10
1098	10	10
1099	11	10
1100	12	10
1101	11	10
1102	11	10
1103	8	10
1104	9	10
1105	10	10
1106	13	10
1107	11	10
1108	10	10
1109	10	10
1110	10	10
1111	10	10
1112	11	10
1113	12	10
1114	12	10
1115	11	10
1116	10	10
1117	11	10
1118	11	10
1119	12	10
1120	204	170
1121	9	10
1122	10	10
1123	11	10
1124	11	10
1125	6	10
1126	11	10
1127	12	10
1128	13	10
1129	13	10
1130	10	10
1131	11	10
1132	13	10
1133	10	10
1134	8	10
1135	10	10
1136	12	10
1137	11	10
1138	12	10
1139	11	10
1140	12	10
1141	13	10
1142	10	10
1143	11	10
1144	9	10
1145	10	10
1146	11	10
1147	12	10
1148	10	10
1149	12	10
1150	9	10
1151	12	10
1152	11	10
1153	12	10
1154	13	10
1155	12	10
1156	11	10
1157	10	10
1158	10	10
1159	10	10
1160	11	10
1161	12	10
1162	12	10
1163	10	10
1164	8	10
1165	4	20
1166	12	10
1167	11	10
1168	11	10
1169	12	10
1170	10	10
1171	10	10
1172	11	10
1173	10	10
1174	9	10
1175	11	10
1176	10	10
1177	12	10
1178	10	10
1179	11	10
1180	10	10
1181	11	10
1182	10	10
1183	12	10
1184	10	10
1185	11	10
1186	10	10
1187	9	10
1188	13	10
1189	3	10
1190	11	10
1191	11	10
1192	10	10
1193	11	10
1194	11	10
1195	9	10
1196	12	10
1197	10	10
1198	10	10
1199	11	10
1200	12	10
1201	13	10
1202	50	50
1203	12	10
1204	11	10
1205	11	10
1206	13	10
1207	10	10
1208	11	10
1209	10	10
1210	12	10
1211	11	10
1212	10	10
1213	10	10
1214	12	10
1215	12	10
1216	9	10
1217	12	10
1218	10	10
1219	4	10
1220	12	10
1221	10	10
1222	10	10
1223	8	10
1224	13	10
1225	10	10
1226	12	10
1227	12	10
1228	99	90
1229	12	10
1230	11	10
1231	10	10
1232	10	10
1233	10	10
1234	10	10
1235	10	10
1236	11	10
1237	12	10
1238	10	10
1239	7	10
1240	11	10
1241	6	10
1243	10	10
1244	11	10
1245	10	10
1246	13	10
1247	9	10
1248	11	10
1249	3	10
1250	11	10
1251	12	10
1252	10	10
1253	12	10
1254	80	80
1255	12	10
1256	12	10
1257	12	10
1258	11	10
1259	11	10
1260	13	10
1261	11	10
1262	8	10
1263	11	10
1264	12	10
1265	12	10
1266	12	10
1267	12	10
1268	12	10
1269	9	10
1270	10	10
1271	8	10
1272	11	10
1273	11	10
1274	45	50
1275	13	10
1276	10	10
1277	9	10
1278	8	10
1279	9	10
1280	10	10
1281	10	10
1282	11	10
1283	11	10
1284	13	10
1285	11	10
1286	12	10
1287	10	10
1288	11	10
1289	10	10
1290	11	10
1291	9	10
1292	10	10
1293	13	10
1294	12	10
1296	11	10
1297	11	10
1298	10	10
1299	12	10
1300	7	10
1301	10	10
1302	12	10
1303	4	10
1304	11	10
1305	10	10
1306	9	10
1307	10	10
1308	10	10
1309	11	10
1310	10	10
1311	12	10
1312	11	10
1313	13	10
1314	3	10
1315	12	10
1316	9	10
1317	10	10
1318	11	10
1319	12	10
1320	12	10
1321	12	10
1322	11	10
1323	11	10
1324	12	10
1325	7	10
1326	13	10
1327	10	10
1328	12	10
1329	10	10
1330	11	10
1331	11	10
1332	12	10
1333	12	10
1334	11	10
1335	11	10
1336	9	10
1337	10	10
1338	12	10
1339	12	10
1340	10	10
1341	12	10
1342	7	10
1343	13	10
1344	12	10
1346	11	10
1347	12	10
1348	10	10
1349	12	10
1350	8	10
1351	60	50
1352	10	10
1353	10	10
1354	11	10
1355	10	10
1356	9	10
1357	10	10
1358	12	10
1359	9	10
1360	12	10
1361	11	10
1362	10	10
1363	6	10
1364	12	10
1365	11	10
1366	10	10
1367	11	10
1368	12	10
1369	13	10
1370	12	10
1371	11	10
1372	9	10
1373	12	10
1374	11	10
1375	11	10
1376	11	10
1377	10	10
1378	9	10
1379	10	10
1380	12	10
1381	11	10
1382	12	10
1383	10	10
1384	7	10
1385	11	10
1386	10	10
1387	12	10
1388	6	10
1389	10	10
1390	11	10
1391	10	10
1392	6	10
1393	10	10
1394	9	10
1395	11	10
1396	12	10
1397	10	10
1398	11	10
1399	5	10
1400	12	10
1401	12	10
1402	10	10
1403	11	10
1404	11	10
1405	10	10
1406	3	10
1407	8	10
1408	12	10
1409	12	10
1410	13	10
1411	13	10
1412	11	10
1413	10	10
1414	10	10
1415	7	10
1416	12	10
1417	9	10
1418	10	10
1419	11	10
1420	11	10
1421	9	10
1422	10	10
1423	11	10
1424	11	10
1425	10	10
1426	10	10
1427	8	10
1428	11	10
1429	11	10
1430	8	10
1431	9	10
1432	11	10
1433	44	40
1434	10	10
1435	8	10
1436	10	10
1437	10	10
1438	12	10
1439	9	10
1440	12	10
1441	9	10
1442	10	10
1443	10	10
1444	9	10
1447	12	10
1448	7	10
1449	10	10
1450	11	10
1451	13	10
1452	13	10
1453	8	10
1454	12	10
1455	10	10
1456	10	10
1457	12	10
1458	11	10
1459	4	10
1460	6	10
1461	5	10
1462	10	10
1463	12	10
1464	10	10
1465	10	10
1466	11	10
1467	9	10
1468	10	10
1469	9	10
1470	11	10
1471	12	10
1472	10	10
1473	7	10
1475	9	10
1476	12	10
1477	10	10
1478	3	10
1480	11	10
1481	10	10
1482	12	10
1483	11	10
1484	9	10
1485	12	10
1486	11	10
1487	10	10
1488	9	10
1489	10	10
1490	10	10
1491	10	10
1492	12	10
1493	13	10
1494	12	10
1495	10	10
1496	10	10
1498	7	10
1499	12	10
1500	10	10
1501	13	10
1502	13	10
1503	9	10
1504	8	10
1505	10	10
1506	10	10
1507	10	10
1508	5	10
1509	12	10
1510	12	10
1511	13	10
1512	11	10
1513	11	10
1514	12	10
1515	12	10
1516	11	10
1517	10	10
1518	12	10
1519	13	10
1520	8	10
1521	10	10
1522	11	10
1524	11	10
1525	10	10
1526	11	10
1527	9	10
1528	12	10
1529	11	10
1530	10	10
1531	12	10
1532	10	10
1533	10	10
1534	12	10
1535	12	10
1536	11	10
1537	9	10
1538	11	10
1539	12	10
1540	8	10
1541	10	10
1542	11	10
1543	12	10
1544	12	10
1545	13	10
1546	9	10
1547	10	10
1548	10	10
1549	10	10
1550	9	10
1551	10	10
1552	11	10
1553	8	10
1554	10	10
1555	7	10
1556	9	10
1557	12	10
1558	8	10
1559	9	10
1560	12	10
1561	12	10
1562	13	10
1563	10	10
1564	10	10
1565	11	10
1566	13	10
1567	12	10
1568	12	10
1569	11	10
1570	11	10
1571	9	10
1572	11	10
1573	7	10
1574	12	10
1575	11	10
1576	10	10
1577	10	10
1578	11	10
1579	7	10
1580	9	10
1581	11	10
1582	11	10
1583	5	10
1584	11	10
1585	11	10
1586	12	10
1587	8	10
1588	12	10
1589	11	10
1590	11	10
1591	10	10
1592	12	10
1593	11	10
1594	10	10
1595	10	10
1596	12	10
1597	11	10
1599	12	10
1600	11	10
1601	3	10
1602	11	10
1603	8	10
1604	9	10
1606	9	10
1607	12	10
1608	7	10
1609	14	10
1610	9	10
1611	10	10
1612	10	10
1613	11	10
1614	11	10
1615	10	10
1616	11	10
1617	11	10
1619	5	10
1620	12	10
1621	11	10
1622	12	10
1623	11	10
1624	5	10
1625	13	10
1626	11	10
1627	10	10
1628	12	10
1629	4	10
1630	12	10
1631	10	10
1632	10	10
1633	12	10
1634	143	130
1635	121	110
1636	6	10
1637	9	10
1638	10	10
1639	10	10
1640	12	10
1641	11	10
1642	10	10
1643	10	10
1644	10	10
1645	5	10
1646	9	10
1647	12	10
1648	9	10
1649	11	10
1650	11	10
1651	11	10
1652	11	10
1653	8	10
1654	10	10
1655	11	10
1656	10	10
1657	9	10
1658	10	10
1659	12	10
1660	9	10
1661	10	10
1662	7	11
1664	12	10
1665	9	10
1666	12	10
1667	11	10
1668	12	10
1669	8	10
1670	7	10
1671	11	10
1672	9	10
1673	9	10
1674	9	10
1675	11	10
1676	12	10
1677	11	10
1678	10	10
1679	11	10
1680	5	10
1681	12	10
1682	10	10
1683	11	10
1684	7	10
1685	12	10
1686	12	10
1687	8	10
1688	12	10
1690	9	10
1691	11	10
1692	3	10
1693	12	10
1694	11	10
1695	9	10
1696	10	10
1697	11	10
1698	11	10
1699	9	10
1700	9	10
1701	4	10
1702	10	10
1703	11	10
1704	9	10
1705	12	10
1706	9	10
1707	10	10
1708	6	10
1709	9	10
1710	8	10
1711	12	10
1712	26	10
1713	10	10
1714	11	10
1715	10	10
1716	12	10
1717	11	10
1718	10	10
1719	12	10
1720	10	10
1721	10	10
1722	9	10
1723	11	10
1724	12	10
1725	9	10
1726	10	10
1727	5	10
1728	10	10
1729	11	10
1730	7	10
1731	10	10
1732	13	10
1733	10	10
1734	9	10
1735	8	10
1736	8	10
1737	7	10
1738	11	10
1739	7	10
1740	8	10
1741	9	10
1742	11	10
1743	10	10
1744	11	10
1745	8	10
1746	10	10
1747	12	10
1748	11	10
1749	9	10
1750	11	10
1751	12	10
1752	11	10
1753	12	10
1754	7	10
1755	11	10
1756	8	10
1757	10	10
1758	10	10
1759	6	10
1760	10	10
1761	2	10
1762	10	10
1763	10	10
1764	2	10
1765	10	10
1766	12	10
1767	12	10
1768	11	10
1769	10	10
1770	12	10
1771	10	10
1772	10	10
1773	9	10
1775	12	10
1776	11	10
1777	10	10
1778	9	10
1779	144	120
1780	10	10
1781	9	10
1782	11	10
1783	9	10
1784	12	10
1785	11	10
1786	10	10
1787	6	10
1788	10	10
1789	12	10
1790	9	10
1791	12	10
1792	9	10
1793	9	10
1794	10	10
1795	10	10
1796	5	10
1797	10	10
1798	11	10
1799	9	10
1800	11	10
1801	10	10
1802	8	10
1803	6	10
1804	12	10
1805	9	10
1806	8	10
1807	10	10
1808	5	10
1809	10	10
1810	11	10
1811	12	10
1812	9	10
1813	10	10
1814	13	10
1815	12	10
1816	12	10
1817	9	10
1818	11	10
1820	5	10
1821	8	10
1822	8	10
1823	12	10
1824	9	10
1825	11	10
1826	11	10
1827	10	10
1828	10	10
1829	8	10
1830	10	10
1831	9	10
1832	10	10
1833	10	10
1834	8	10
1835	11	10
1836	3	10
1837	9	10
1838	10	10
1839	6	10
1840	10	10
1841	11	10
1842	11	10
1843	88	80
1845	10	10
1846	10	10
1847	11	10
1848	10	10
1849	11	10
1850	12	10
1851	12	10
1852	11	10
1853	10	10
1854	11	10
1855	10	10
1856	10	10
1857	12	10
1858	13	10
1859	11	10
1860	10	10
1861	5	10
1862	12	10
1863	13	10
1864	12	10
1865	13	10
1866	13	10
1867	12	10
1868	10	10
1869	1	10
1870	12	10
1871	10	10
1872	7	10
1873	9	10
1874	5	10
1875	10	10
1876	8	10
1877	9	10
1878	11	10
1879	10	10
1880	10	10
1881	12	10
1882	13	10
1883	10	10
1884	11	10
1885	13	10
1886	13	10
1887	9	10
1888	8	10
1889	11	10
1890	12	10
1891	10	10
1892	12	10
1893	12	10
1894	10	10
1896	11	10
1897	10	10
1898	3	10
1899	9	10
1900	10	10
1901	5	10
1902	11	10
1903	12	10
1904	5	10
1906	13	10
1907	10	10
1908	9	10
1909	12	10
1910	10	10
1911	10	10
1912	8	10
1913	10	10
1915	8	10
1916	12	10
1917	11	10
1918	10	10
1919	8	10
1920	2	10
1921	9	10
1922	10	10
1923	11	10
1924	11	10
1925	5	10
1926	10	10
1927	10	10
1928	3	10
1929	12	10
1930	12	10
1931	9	10
1932	10	10
1933	10	10
1934	11	10
1935	10	10
1936	10	10
1937	11	10
1938	3	10
1939	10	10
1941	4	10
1942	10	10
1943	12	10
1944	12	10
1945	10	10
1946	8	10
1947	3	10
1948	8	10
1949	11	10
1950	9	10
1951	11	10
1952	13	10
1953	12	10
1954	10	10
1955	10	10
1956	7	10
1957	11	10
1958	8	10
1959	7	10
1960	11	10
1961	8	10
1962	11	10
1963	12	10
1964	9	10
1965	7	10
1966	11	10
1967	10	10
1968	11	10
1969	10	10
1970	8	10
1971	11	10
1972	6	10
1973	10	10
1974	8	10
1975	12	10
1976	8	10
1977	10	10
1978	9	10
1979	5	10
1980	10	10
1981	7	10
1982	11	10
1983	10	10
1984	9	10
1985	11	10
1986	6	10
1987	8	10
1988	10	10
1989	9	10
1990	9	10
1991	12	10
1992	6	10
1993	9	10
1994	11	10
1995	9	10
1996	10	10
1997	8	10
1998	7	10
1999	10	10
2000	10	10
2001	10	10
2002	12	10
2003	8	10
2004	12	10
2005	11	10
2006	10	10
2007	10	10
2008	8	10
2009	11	10
2010	10	10
2011	12	10
2012	7	10
2013	5	10
2014	9	10
2015	9	10
2016	10	10
2017	8	10
2018	12	10
2019	11	10
2020	9	10
2021	11	10
2022	7	10
2023	10	10
2024	11	10
2025	11	10
2026	5	10
2027	10	10
2028	10	10
2029	8	10
2030	13	10
2031	10	10
2032	9	10
2033	6	10
2034	11	10
2035	11	10
2036	13	10
2037	13	10
2039	10	10
2040	6	10
2041	10	10
2042	8	10
2043	9	10
2044	6	10
2045	10	10
2046	10	10
2047	9	10
2048	8	10
2049	11	10
2050	11	10
2051	8	10
2052	10	10
2053	11	10
2054	7	10
2055	11	10
2056	9	10
2057	10	10
2058	9	10
2059	10	10
2060	12	10
2061	10	10
2062	8	10
2063	5	10
2064	11	10
2065	12	10
2066	9	10
2067	9	10
2068	10	10
2069	8	10
2070	4	10
2071	6	10
2072	8	10
2073	12	10
2074	420	10
2075	10	10
2076	4	10
2077	7	10
2078	10	10
2079	2	10
2080	12	10
2081	10	10
2082	10	10
2083	10	10
2084	11	10
2085	10	10
2086	10	10
2087	11	10
2088	7	10
2089	9	10
2090	8	10
2091	1	10
2092	5	10
2093	7	10
2094	10	10
2095	10	10
2096	9	10
2097	8	10
2098	10	10
2099	12	10
2100	6	10
2101	8	10
2102	8	10
2103	8	10
2104	10	10
2105	10	10
2106	10	10
2107	7	10
2108	11	10
2109	5	10
2110	10	10
2111	11	10
2112	10	10
2113	11	10
2114	10	10
2115	11	10
2116	9	10
2117	12	10
2118	10	10
2119	6	10
2120	12	10
2121	10	10
2122	10	10
2123	8	10
2124	12	10
2125	9	10
2126	10	10
2127	11	10
2128	9	10
2129	11	10
2130	9	10
2131	10	10
2132	10	10
2133	10	10
2134	5	10
2135	11	10
2136	3	10
2137	11	10
2138	10	10
2139	5	10
2140	10	10
2141	9	10
2142	10	10
2143	10	10
2144	11	10
2145	12	10
2146	10	10
2147	10	10
2148	8	10
2150	10	10
2151	9	10
2152	8	10
2153	5	10
2154	12	10
2155	10	10
2156	10	10
2157	6	10
2158	10	10
2159	7	10
2160	9	10
2161	10	10
2162	10	10
2163	6	10
2164	10	10
2165	10	10
2166	10	10
2167	11	10
2168	8	10
2169	10	10
2170	10	10
2171	12	10
2172	13	10
2173	11	10
2174	8	10
2175	10	10
2176	9	10
2177	10	10
2178	10	10
2179	12	10
2180	11	10
2181	5	10
2182	8	10
2183	3	10
2184	7	10
2185	9	10
2186	4	10
2187	12	10
2188	11	10
2190	10	10
2191	10	10
2192	9	10
2193	11	10
2194	11	10
2195	11	10
2196	11	10
2197	10	10
2198	7	10
2199	10	10
2200	11	10
2201	11	10
2202	3	10
2203	10	10
2204	10	10
2205	10	10
2206	5	10
2207	9	10
2208	10	10
2209	10	10
2210	10	10
2211	7	10
2212	13	10
2213	11	10
2214	10	10
2215	10	10
2216	8	10
2217	12	10
2218	10	10
2219	8	10
2220	9	10
2221	11	10
2222	4	10
2223	9	10
2224	10	10
2225	11	10
2226	10	10
2227	10	10
2228	13	10
2229	8	10
2230	10	10
2231	10	10
2232	10	10
2233	8	10
2234	12	10
2235	7	10
2236	9	10
2237	2	10
2238	6	10
2239	3	10
2240	9	10
2241	10	10
2242	5	10
2243	9	10
2244	11	10
2245	10	10
2246	2	10
2247	10	10
2248	10	10
2249	9	10
2250	12	10
2251	10	10
2252	12	10
2253	8	10
2254	9	10
2255	8	10
2256	9	10
2257	11	10
2258	7	10
2261	1	10
2262	9	10
2263	10	10
2264	9	10
2265	8	10
2266	10	10
2267	12	10
2268	8	10
2269	12	10
2270	11	10
2271	9	10
2272	7	10
2273	11	10
2274	7	10
2275	11	10
2276	6	10
2277	10	10
2278	12	10
2279	7	10
2280	8	10
2281	10	10
2282	9	10
2283	11	10
2284	12	10
2285	10	10
2286	10	10
2287	8	10
2288	4	10
2289	9	10
2290	10	10
2291	10	10
2292	11	10
2293	12	10
2294	10	10
2295	10	10
2296	7	10
2297	10	10
2299	8	10
2300	10	10
2301	12	10
2302	9	10
2303	10	10
2304	11	10
2305	3	10
2306	10	10
2307	12	10
2308	9	10
2309	8	10
2310	2	10
2311	10	10
2312	5	10
2313	10	10
2314	8	10
2315	8	10
2316	4	10
2317	9	10
2318	10	10
2319	9	10
2320	7	10
2321	10	10
2322	6	10
2323	7	10
2324	12	10
2325	10	10
2326	2	10
2327	7	10
2328	9	10
2329	11	10
2330	6	10
2331	8	10
2332	10	10
2333	9	10
2334	3	10
2335	1	2
2336	11	10
2337	10	10
2338	1	10
2339	11	10
2340	8	10
2341	9	10
2342	6	10
2343	10	10
2344	9	10
2345	10	10
2346	8	10
2347	9	10
2348	10	10
2349	2	10
2350	10	10
2351	5	10
2352	6	10
2353	9	10
2354	7	10
2355	8	10

	tweet_id	timestamp	source	text	expanded_urls	rating_numerator	rating_denominator	name	dog_stage
0	892420643555336193	2017-08-01 16:23:56	Twitter for iPhone	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	NaN
1	892177421306343426	2017-08-01 00:17:27	Twitter for iPhone	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	NaN
2	891815181378084864	2017-07-31 00:18:03	Twitter for iPhone	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	NaN
3	891689557279858688	2017-07-30 15:58:51	Twitter for iPhone	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	NaN
4	891327558926688256	2017-07-29 16:00:24	Twitter for iPhone	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	NaN

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	1.566650e-01	True	Shetland_sheepdog	6.142850e-02	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	7.419170e-02	True	Rhodesian_ridgeback	7.201000e-02	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	1.385840e-01	True	bloodhound	1.161970e-01	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	3.606870e-01	True	miniature_pinscher	2.227520e-01	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	2.436820e-01	True	Doberman	1.546290e-01	True
5	666050758794694657	https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg	1	Bernese_mountain_dog	0.651137	True	English_springer	2.637880e-01	True	Greater_Swiss_Mountain_dog	1.619920e-02	True
6	666051853826850816	https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg	1	box_turtle	0.933012	False	mud_turtle	4.588540e-02	False	terrapin	1.788530e-02	False
7	666055525042405380	https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg	1	chow	0.692517	True	Tibetan_mastiff	5.827940e-02	True	fur_coat	5.444860e-02	False
8	666057090499244032	https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg	1	shopping_cart	0.962465	False	shopping_basket	1.459380e-02	False	golden_retriever	7.958960e-03	True
9	666058600524156928	https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg	1	miniature_poodle	0.201493	True	komondor	1.923050e-01	True	soft-coated_wheaten_terrier	8.208610e-02	True
10	666063827256086533	https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg	1	golden_retriever	0.775930	True	Tibetan_mastiff	9.371780e-02	True	Labrador_retriever	7.242660e-02	True
11	666071193221509120	https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg	1	Gordon_setter	0.503672	True	Yorkshire_terrier	1.742010e-01	True	Pekinese	1.094540e-01	True
12	666073100786774016	https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg	1	Walker_hound	0.260857	True	English_foxhound	1.753820e-01	True	Ibizan_hound	9.747050e-02	True
13	666082916733198337	https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg	1	pug	0.489814	True	bull_mastiff	4.047220e-01	True	French_bulldog	4.895950e-02	True
14	666094000022159362	https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg	1	bloodhound	0.195217	True	German_shepherd	7.825980e-02	True	malinois	7.562780e-02	True
15	666099513787052032	https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg	1	Lhasa	0.582330	True	Shih-Tzu	1.661920e-01	True	Dandie_Dinmont	8.968830e-02	True
16	666102155909144576	https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg	1	English_setter	0.298617	True	Newfoundland	1.498420e-01	True	borzoi	1.336490e-01	True
17	666104133288665088	https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg	1	hen	0.965932	False	cock	3.391940e-02	False	partridge	5.206580e-05	False
18	666268910803644416	https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg	1	desktop_computer	0.086502	False	desk	8.554740e-02	False	bookcase	7.947970e-02	False
19	666273097616637952	https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg	1	Italian_greyhound	0.176053	True	toy_terrier	1.118840e-01	True	basenji	1.111520e-01	True
20	666287406224695296	https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpg	1	Maltese_dog	0.857531	True	toy_poodle	6.306380e-02	True	miniature_poodle	2.558060e-02	True
21	666293911632134144	https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg	1	three-toed_sloth	0.914671	False	otter	1.525000e-02	False	great_grey_owl	1.320720e-02	False
22	666337882303524864	https://pbs.twimg.com/media/CT9OwFIWEAMuRje.jpg	1	ox	0.416669	False	Newfoundland	2.784070e-01	True	groenendael	1.026430e-01	True
23	666345417576210432	https://pbs.twimg.com/media/CT9Vn7PWoAA_ZCM.jpg	1	golden_retriever	0.858744	True	Chesapeake_Bay_retriever	5.478680e-02	True	Labrador_retriever	1.424090e-02	True
24	666353288456101888	https://pbs.twimg.com/media/CT9cx0tUEAAhNN_.jpg	1	malamute	0.336874	True	Siberian_husky	1.476550e-01	True	Eskimo_dog	9.341240e-02	True
25	666362758909284353	https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg	1	guinea_pig	0.996496	False	skunk	2.402450e-03	False	hamster	4.608630e-04	False
26	666373753744588802	https://pbs.twimg.com/media/CT9vZEYWUAAlZ05.jpg	1	soft-coated_wheaten_terrier	0.326467	True	Afghan_hound	2.595510e-01	True	briard	2.068030e-01	True
27	666396247373291520	https://pbs.twimg.com/media/CT-D2ZHWIAA3gK1.jpg	1	Chihuahua	0.978108	True	toy_terrier	9.396970e-03	True	papillon	4.576810e-03	True
28	666407126856765440	https://pbs.twimg.com/media/CT-NvwmW4AAugGZ.jpg	1	black-and-tan_coonhound	0.529139	True	bloodhound	2.442200e-01	True	flat-coated_retriever	1.738100e-01	True
29	666411507551481857	https://pbs.twimg.com/media/CT-RugiWIAELEaq.jpg	1	coho	0.404640	False	barracouta	2.714850e-01	False	gar	1.899450e-01	False
30	666418789513326592	https://pbs.twimg.com/media/CT-YWb7U8AA7QnN.jpg	1	toy_terrier	0.149680	True	papillon	1.482580e-01	True	Chihuahua	1.428600e-01	True
31	666421158376562688	https://pbs.twimg.com/media/CT-aggCXAAIMfT3.jpg	1	Blenheim_spaniel	0.906777	True	cocker_spaniel	9.034640e-02	True	Shih-Tzu	1.116870e-03	True
32	666428276349472768	https://pbs.twimg.com/media/CT-g-0DUwAEQdSn.jpg	1	Pembroke	0.371361	True	chow	2.493940e-01	True	Pomeranian	2.418780e-01	True
33	666430724426358785	https://pbs.twimg.com/media/CT-jNYqW4AAPi2M.jpg	1	llama	0.505184	False	Irish_terrier	1.041090e-01	True	dingo	6.207120e-02	False
34	666435652385423360	https://pbs.twimg.com/media/CT-nsTQWEAEkyDn.jpg	1	Chesapeake_Bay_retriever	0.184130	True	chain_saw	5.677530e-02	False	power_drill	3.676340e-02	False
35	666437273139982337	https://pbs.twimg.com/media/CT-pKmRWIAAxUWj.jpg	1	Chihuahua	0.671853	True	beagle	1.246800e-01	True	Saluki	4.409420e-02	True
36	666447344410484738	https://pbs.twimg.com/media/CT-yU5QWwAEjLX5.jpg	1	curly-coated_retriever	0.322084	True	giant_schnauzer	2.879550e-01	True	Labrador_retriever	1.663310e-01	True
37	666454714377183233	https://pbs.twimg.com/media/CT-5Bs-WUAA2JeC.jpg	1	dalmatian	0.278954	True	Labrador_retriever	2.376120e-01	True	Great_Pyrenees	1.711060e-01	True
38	666644823164719104	https://pbs.twimg.com/media/CUBl6IwVAAA9_zT.jpg	1	Ibizan_hound	0.044333	True	Pembroke	4.320930e-02	True	West_Highland_white_terrier	3.890560e-02	True
39	666649482315059201	https://pbs.twimg.com/media/CUBqKnLWwAA5OQB.jpg	1	Border_collie	0.447803	True	English_springer	1.704970e-01	True	collie	1.392060e-01	True

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	1.566650e-01	True	Shetland_sheepdog	6.142850e-02	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	7.419170e-02	True	Rhodesian_ridgeback	7.201000e-02	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	1.385840e-01	True	bloodhound	1.161970e-01	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	3.606870e-01	True	miniature_pinscher	2.227520e-01	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	2.436820e-01	True	Doberman	1.546290e-01	True
5	666050758794694657	https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg	1	Bernese_mountain_dog	0.651137	True	English_springer	2.637880e-01	True	Greater_Swiss_Mountain_dog	1.619920e-02	True
6	666051853826850816	https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg	1	box_turtle	0.933012	False	mud_turtle	4.588540e-02	False	terrapin	1.788530e-02	False
7	666055525042405380	https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg	1	chow	0.692517	True	Tibetan_mastiff	5.827940e-02	True	fur_coat	5.444860e-02	False
8	666057090499244032	https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg	1	shopping_cart	0.962465	False	shopping_basket	1.459380e-02	False	golden_retriever	7.958960e-03	True
9	666058600524156928	https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg	1	miniature_poodle	0.201493	True	komondor	1.923050e-01	True	soft-coated_wheaten_terrier	8.208610e-02	True
10	666063827256086533	https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg	1	golden_retriever	0.775930	True	Tibetan_mastiff	9.371780e-02	True	Labrador_retriever	7.242660e-02	True
11	666071193221509120	https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg	1	Gordon_setter	0.503672	True	Yorkshire_terrier	1.742010e-01	True	Pekinese	1.094540e-01	True
12	666073100786774016	https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg	1	Walker_hound	0.260857	True	English_foxhound	1.753820e-01	True	Ibizan_hound	9.747050e-02	True
13	666082916733198337	https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg	1	pug	0.489814	True	bull_mastiff	4.047220e-01	True	French_bulldog	4.895950e-02	True
14	666094000022159362	https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg	1	bloodhound	0.195217	True	German_shepherd	7.825980e-02	True	malinois	7.562780e-02	True
15	666099513787052032	https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg	1	Lhasa	0.582330	True	Shih-Tzu	1.661920e-01	True	Dandie_Dinmont	8.968830e-02	True
16	666102155909144576	https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg	1	English_setter	0.298617	True	Newfoundland	1.498420e-01	True	borzoi	1.336490e-01	True
17	666104133288665088	https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg	1	hen	0.965932	False	cock	3.391940e-02	False	partridge	5.206580e-05	False
18	666268910803644416	https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg	1	desktop_computer	0.086502	False	desk	8.554740e-02	False	bookcase	7.947970e-02	False
19	666273097616637952	https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg	1	Italian_greyhound	0.176053	True	toy_terrier	1.118840e-01	True	basenji	1.111520e-01	True
20	666287406224695296	https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpg	1	Maltese_dog	0.857531	True	toy_poodle	6.306380e-02	True	miniature_poodle	2.558060e-02	True
21	666293911632134144	https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg	1	three-toed_sloth	0.914671	False	otter	1.525000e-02	False	great_grey_owl	1.320720e-02	False
22	666337882303524864	https://pbs.twimg.com/media/CT9OwFIWEAMuRje.jpg	1	ox	0.416669	False	Newfoundland	2.784070e-01	True	groenendael	1.026430e-01	True
23	666345417576210432	https://pbs.twimg.com/media/CT9Vn7PWoAA_ZCM.jpg	1	golden_retriever	0.858744	True	Chesapeake_Bay_retriever	5.478680e-02	True	Labrador_retriever	1.424090e-02	True
24	666353288456101888	https://pbs.twimg.com/media/CT9cx0tUEAAhNN_.jpg	1	malamute	0.336874	True	Siberian_husky	1.476550e-01	True	Eskimo_dog	9.341240e-02	True
25	666362758909284353	https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg	1	guinea_pig	0.996496	False	skunk	2.402450e-03	False	hamster	4.608630e-04	False
26	666373753744588802	https://pbs.twimg.com/media/CT9vZEYWUAAlZ05.jpg	1	soft-coated_wheaten_terrier	0.326467	True	Afghan_hound	2.595510e-01	True	briard	2.068030e-01	True
27	666396247373291520	https://pbs.twimg.com/media/CT-D2ZHWIAA3gK1.jpg	1	Chihuahua	0.978108	True	toy_terrier	9.396970e-03	True	papillon	4.576810e-03	True
28	666407126856765440	https://pbs.twimg.com/media/CT-NvwmW4AAugGZ.jpg	1	black-and-tan_coonhound	0.529139	True	bloodhound	2.442200e-01	True	flat-coated_retriever	1.738100e-01	True
29	666411507551481857	https://pbs.twimg.com/media/CT-RugiWIAELEaq.jpg	1	coho	0.404640	False	barracouta	2.714850e-01	False	gar	1.899450e-01	False
30	666418789513326592	https://pbs.twimg.com/media/CT-YWb7U8AA7QnN.jpg	1	toy_terrier	0.149680	True	papillon	1.482580e-01	True	Chihuahua	1.428600e-01	True
31	666421158376562688	https://pbs.twimg.com/media/CT-aggCXAAIMfT3.jpg	1	Blenheim_spaniel	0.906777	True	cocker_spaniel	9.034640e-02	True	Shih-Tzu	1.116870e-03	True
32	666428276349472768	https://pbs.twimg.com/media/CT-g-0DUwAEQdSn.jpg	1	Pembroke	0.371361	True	chow	2.493940e-01	True	Pomeranian	2.418780e-01	True
33	666430724426358785	https://pbs.twimg.com/media/CT-jNYqW4AAPi2M.jpg	1	llama	0.505184	False	Irish_terrier	1.041090e-01	True	dingo	6.207120e-02	False
34	666435652385423360	https://pbs.twimg.com/media/CT-nsTQWEAEkyDn.jpg	1	Chesapeake_Bay_retriever	0.184130	True	chain_saw	5.677530e-02	False	power_drill	3.676340e-02	False
35	666437273139982337	https://pbs.twimg.com/media/CT-pKmRWIAAxUWj.jpg	1	Chihuahua	0.671853	True	beagle	1.246800e-01	True	Saluki	4.409420e-02	True
36	666447344410484738	https://pbs.twimg.com/media/CT-yU5QWwAEjLX5.jpg	1	curly-coated_retriever	0.322084	True	giant_schnauzer	2.879550e-01	True	Labrador_retriever	1.663310e-01	True
37	666454714377183233	https://pbs.twimg.com/media/CT-5Bs-WUAA2JeC.jpg	1	dalmatian	0.278954	True	Labrador_retriever	2.376120e-01	True	Great_Pyrenees	1.711060e-01	True
38	666644823164719104	https://pbs.twimg.com/media/CUBl6IwVAAA9_zT.jpg	1	Ibizan_hound	0.044333	True	Pembroke	4.320930e-02	True	West_Highland_white_terrier	3.890560e-02	True
39	666649482315059201	https://pbs.twimg.com/media/CUBqKnLWwAA5OQB.jpg	1	Border_collie	0.447803	True	English_springer	1.704970e-01	True	collie	1.392060e-01	True

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